Analysis and Deployment of an OCR - SSD Deep Learning Technique for Real-Time Active Car Tracking and Positioning on a Quadrotor

Luiz Gustavo Miranda Pinto1

1 Institute of Mathematics and Computation – IMC, Federal University of Itajubá. Brazil 2

ABSTRACT

This work presents a deep learning solution object tracking and object detection in images and real-time license plate recognition implemented in F450 quadcopter in autonomous flight. The solution uses Python programming language, OpenCV library, remote PX4 control with MAVSDK, OpenALPR, neural network using Caffe and TensorFlow.

1. INTRODUCTION

A drone can follow an object that updates its route all the time. This is called active tracking and positioning, where an autonomous vehicle needs to follow a goal without assistance from human intervention. There are some approaches to this mission with drones, but it is rarely used for object detection and OCR due to resource consumption. State-of-the-art algorithms can identify the class of a target object being followed. This work presents and analyzes a technique that grants control to a drone during an autonomous flight, using real-time tracking and positioning through an OCR system for deep learning model of plate detection and object detection.

2. MATERIALS AND METHODS

The following will present the concepts, techniques, models, materials and methods used in the proposed system, in addition to the structures and platforms used for its creation.

2.1. Rotary Wing UAV

This project used an F-450 quadcopter drone for outdoor testing and a Typhoon H480 octorotor for the simulation. A quadcopter is an aircraft made up of 4 rotors carrying the controller board in the middle and the rotors at the ends. It is controlled by changing the angular speeds of the rotors that are rotated by electro- magnetic motors, where you can have 6 degrees of freedom, as seen in Figure 1, with x, y and z as the transition movement, and roll, pitch and yaw as the rotation- al movement.

The altitude and position of the drone can be modified by adjusting engine speeds. The same applies to pitch control but controlling the rear or front engines. 3

Figure 1 – Six degrees of freedom. Source: (STRIMEL; BARTHOLOMEW; KIM, 2017)

2.1.1. Pixhawk Fligh Control Hardware This project was implemented using the Pixhawk flight controller (Figure 2), an independent open hardware flight controller. Pixhawk supports manual and automatic operations, being suitable for research, because it is inexpensive and compatible with most remote control transmitters and receivers. Pixhawk is built with a dual processor with 32-bit computing capacity STM32f427 Cortex M4 MHz processor / 256 cores 168KB of RAM / 2MB of flash bit. The current version is the Pixhawk 4.

Figure 2 – Pixhalk 2.4.8 flight controller. Source: (KANTUE; PEDRO, 2019)

2.1.2. PX4 Flight Control Software In this work, the PX4 flight control software was used, because is the Pix- hawk's native software, avoiding compatibility problems. PX4 is open source flight control software for drones and other unmanned vehicles that provides a set of tools to create customized solutions.

There are several internal types of frames with their own flight control parameters, including engine assignment and numbering, which includes the F450 used in this project. These parameters can be modified to obtain refinement during a flight. In the case of PX4, it uses Proportional, Integral, Derivative (PID) controllers, which are one of the most widespread control techniques.

In the PID controllers, the P (proportional) gain is used to minimize the tracking error. It is responsible for a quick response and therefore should be as high as possible. Gain (derivative) is used to moisten. It is necessary, but only the maximum necessary to avoid overtaking must be defined. Gain I (integral) maintains an error memory. The term “I” increases when desired the rate has not been reached for some time. The idea is to parameterize the in-flight board model using GCS (Ground Control Station) software, where it is possible change these parameters and check their effects on each of the drone´s degrees of freedom.

2.2. Ground Control Station

A ground control station (GCS), described in the previous chapter, needs to check the behavior of the drone during the flight and was used to update the drone's firmware, adjust its parameters and calibrate your sensors. Running on a base computer, communicate with the UAV wirelessly, such as telemetry or Wi- Fi, display real-time data on the performance and position of the UAV, and show data from many instruments present on a conventional plane or helicopter.

This work used the QGroundControl GCS due to its affinity with the PX4 and PID tools, which allowed changes to the PID while the drone was still in the air. QGroundControl has the ability to read telemetry data simultaneously from multiple aircrafts if they are using the same version of MAVLink, while still being more common features such as telemetry logging, visual display of the GUI, a mission planning tool, the ability to adjust the PID gains during the flight, as shown in Figure 3, and the ability to display vital information flight data information. 5

Figure 3 – QGroundControl PID tuning Source: (QGROUNDCONTROL, 2019b)

2.3. Drone Software Development Kit (SDK)

During the development of this project, some SDKs were used to obtain autonomous control of software on drones. They were used in different parts of the project and the reason it was the versatility of each other. The idea was to choose the SDK that had the best balance between robustness and simplicity. The selection included MAVROS, MAVSDK and DroneKit because of their popularity. All SDKs included the MAVLink protocol, responsible for controlling the drone.

DroneKit presented the best simplicity, but it did not have the best support for the PX4. External control that accepts remote commands via the programming was developed for ArduPilot applications 10.

The MAVROS package running ROS presented the best system in terms of robustness, but it was complex to manage. MAVSDK presented the best result. It has full support for PX4 applications and is not complex to manage, being the sub- ject of choice for this project.

2.3.1. MAVLink and MAVSDK MAVLink is a very useful messaging protocol for exchanging information with a drone and between the controller board components. During its use, data flows, such as telemetry status, are published as topics, while subprotocol, like those used for mission or parameter control, are point to point with retransmission.

MAVSDK is a MAVLink library with APIs implemented in different languages, such as C ++ and Python. MAVSDK can be used on a computer embed- ded in a vehicle, at a land or mobile station device, which has significantly more processing power than an ordinary flight controller, be able to perform tasks such as computer vision, obstacle prevention and route planning. MAVSDK was the chosen framework, due to its affinity with the PX4 and its simplicity.

2.3.2. ROS and MAVROS The Robot Operating System (ROS), also presented in the previous chapter, is a structure widely used in robotics. ROS offers features such as distributed computing, message passing and code reuse.

ROS allows the robot control to be divided into several tasks called nodes and are processes that perform calculations, allowing modularity. The nodes exchange messages with each other in an editor-subscriber system, where a topic acts as an intermediate store for some of the nodes to publish its content while others sub- scribe to receive this content.

ROS has a general manager called “master”. The ROS master, as seen in Figure 4, is responsible for providing names and records to services and nodes in the ROS system. It tracks and directs editors and subscribers to topics and Services. The role of the master is to guide the individual ROS nodes to locate each from others. After the nodes are located, they define their communication via point to point.

Figure 4 – The ROS Master Source: (HASAN, 2019)

To support collaborative development, the ROS system is organized in packages, which are simply directories that contain XML files that describe the package 7

and presenting any dependencies. One of these packages is the MAVROS, which is an extensible communication node MAVLink with proxy for GCS. The package provides a driver for communication between a variety of autopilots with MAVLink communication protocol and a UDP MAVLink bridge for GCS. The MAVROS package allows MAVLink communication between different comput- ers running ROS, being currently the official supported bridge between ROS and MAVLink.

2.3.3. DroneKit DroneKit is an SDK built with development tools for drones. It allows to create applications which runs on a host computer and allows communication with Ar- duPilot flight controllers. Applications can to insert a level of intelligence into the vehicle's behavior, and can perform tasks with high computational performance they cost or depend in real time, such as computer vision or path planning.

Currently, the PX4 is not yet fully compatible, being more suitable for ArduPi- lot applications.

2.4. Simulation

In this project, the Gazebo platform was the choice to imitate environment through PX4 SITL. However, it was not the only option, since the PX4 SITL is available for other platforms, such as jMAVSim, AirSim and X-Plane. JMAVSim was not easy to integrate obstacles or Extra sensors, such as cameras, are discarded mainly for this purpose reason. AirSim was also discarded because, while realistic, it requires a powerful GPU (Graphics Processing Unit), which could com- pete for resources during the object detection phase.

The X-Plane, also discarded, is realistic and has a variety of UAV models and environments already implemented, however, it depends on the licensing for its use. Thus, Gazebo was the option chosen, due to its simulation environment with a variety of online resource models, the ability to import meshes from other modeling software, such as Solidworks or Inkscape, and the free license.

2.4.1. Gazebo Gazebo, already presented in the previous chapter, is an open source simulation platform that allows integration with ROS. Gazebo is currently only supported on Linux, but there are some speculation about a version of Windows 8

2.4.2. PX4 SITL The PX4 firmware offers a complete hardware simulation, with a response that provides the entry of the environment using its own SITL (Software in the loop). The simulation reacts to the simulation data given exactly how it would react in reality and evaluates the total production energy required in each rotor.

The PX4 SITL allows you to simulate the same software as on a real platform, rigorously replicating the behaviors of an autopilot. Can simulate the same autopilot used on a real drone and its MAVLink protocol, which generalizes direct use of a real drone. The greatest the PX4 SITL is that a flight controller cannot distin- guish whether it is running on simulation or inside a real vehicle, allowing the simulation code to be imported directly for commercially available UAV platforms.

2.5. Deep Learning Frameworks

Deep learning is a type of machine learning, generally used for classification, regression and resource extraction tasks, with multiple layers of representation and abstraction. For object detection, resource extraction tasks are required and can be achieved using convolutional neural networks (CNN), a class of deep neural networks that apply filters at various levels to extract and classify visual information from a source, such as an image or video. This project used a CNN to detect visual targets using a camera.

2.5.1. Caffe Framework In this project was used the Caffe deep learning framework. Caffe provides a complete toolkit for training, testing, fine-tuning and model deployment, with well-written documentation examples for these tasks. It is developed under a free BSD license, being built with the C++ language and maintaining Python and MATLAB links for training and deploy general purpose convolutional neural networks and many other deep models efficiently.

2.6. Object Detection

The most common way to interpret the location of the object is to create a bounding box around the detected object, as seen in Figure 5.

Figure 5 – Bounding boxes from object detection Source: (GANESH, 2019)

Object detection, detailed in the previous chapter, was the first stage in this tracking system, as it focuses on the object to be tracked.

2.6.1. Convolutional Neural Networks (CNN) A convolutional neural network (CNN), as described in the previous chapter, is a variation of the so called multilayer perceptron networks and was inspired by the biological process of data processing.

2.6.2. Single-Shot MultiBox Detectot – SSD SSD is an object detection algorithm that uses a deep learning model for neural networks. It was designed for real-time applications, like this one. It is lighter than other models, as it speeds up the process of inferring new bounding boxes reuse of pixels or feature maps, which are the result of convolutional blocks and representation of the dominant characteristics of the image at different scales.

Its core was built around a technique called MultiBox, which is a method for proposals for coordinates of class agnostic bounding boxes. Regarding its performance and accuracy, for applicability in object detection, it has a score above 74% mAP at 59 frames per second in data sets like COCO and PascalVOC. 10

2.6.2.1. MultiBox MultiBox is a method for bounding box regression that achieves dimensionality reduction, as it consists of branches of convolutional layers, as seen in Figure 6 that resize images over the network, maintaining the original width and height.

Figure 6 – MultiBox architecture Source: (FORSON, 2017)

The magic behind the MultiBox technique is the interaction between two criti- cal assessment factors: loss of confidence (CL) and loss of location (LL). CL is a factor that measures how confident the class selection is made, which means whether it is the correct class of the object, using categorical cross entropy in rela- tion to entropy. We can consider cross entropy as a received response that is not optimal. Entropy on the other hand, it represents the ideal answer. Therefore, knowing entropy, all entropy received can be measured in terms of how far these responses are from the ideal.

2.6.2.2. SSD Architecture The SSD is composed by the extraction of maps of resources, through an intermediate neural network called a resource extractor and the application of convolution filters to detect objects. The SSD architecture consists of 3 main components: basic network, extra layers for resources extraction and forecasting layers.

Figure 7 – VG-16 architecture Source: (FROSSARD, 2016) In the basic network, extraction is performed using a convolutional neural network called VGG-16 - the resource extractor, as seen in Figure 7, where it is made up of combinations of convolution layers with ReLU for fully connected layers. In addition, in addition, it has layers of maximum pool and a final layer with a soft- max activation function. The output of this network is a resource map with dimensions 19x19x1024. Right after the basic network, 4 additional additional convolutional layers are added to continue reducing the size of the resource map until its final dimensions are 1 x 1 x 256. Finally, the forecast layers, a crucial element of the SSD, use a variety of resource maps representing various scales to predict class scores and bounding box coordinates. The final composition of the SSD can be seen in Figure 8.

Figure 8 – SSD architecture Source: (DALMIA, 2019)

2.7. Optical Character Recognition - OCR

In this work, the identification of the vehicle license plate was important for the drone be able to follow a car with the correct license plate. This problem occurred with the optical character recognition system (OCR). OCR is a technique responsible for recognizing optically drawn characters. OCR is a complex problem to deal with due to the variety of languages, fonts and styles in which characters and information can be written, including the complex rules for each of these languages.

2.7.1. Inference Process An example of the steps in the OCR technique is shown in Figure 9. The steps are as follows: Acquisition, Pre-processing, Segmentation, Resource Extraction and Recognition. 12

 Acquisition: a recorded image is fed into the system.

Figure 9 – OCR inference process steps Source: (ADAMS, 1854)

 Pre-processing: eliminates color variations by smoothing and normalizing pixels. Smoothing applies convolution filters to the image to remove noise and smooth the edges. Normalization finds a uniform size, slope and rotation for all characters in the image.

 Segmentation: find the words written inside the image.

 Resource extraction: extracts the characteristics of the symbols.

 Recognition: actually identifies the characters and classifies them, search- ing the lines, word for word, converting the images for character streams representing letters of recognized words.

2.7.2. Teressact OCR This work used a plate detection called OpenALPR that uses Google Tesseract, an open source OCR framework to train networks for different languages and scripts. It convert the image into binary images. Identifies and extracts character outlines. Transforms Blobs contours, which are small regions isolated from digital images. Divide text into words using techniques like cloudy spaces and defined spaces. Finally, it recognizes the text by classifying and storing each recognized word.

2.7.3. Automatic License Plate Recognition - ALPR ALPR is a way to detect characters that makes up the license plate of the vehicle and uses OCR for most of the process. It combines object detection, image processing and pattern recognition. It is used in real-life applications, such as automatic toll collection, traffic law enforcement, access control to parking lots and road traffic monitoring. The four steps of the ALPR are shown in Figure 10.

Figure 10 – ALPR steps Source: (SARFRAZ; AHMED; GHAZI, 2003)

2.7.3.1. OpenALPR This project used OpenALPR, an open source ALPR library built with the C ++ language and has links in C #, Java, Node.js, and Python. The library receives images and video streams for analysis in order to identify registrations and generates a text representing the enrollment characters. It is based on OpenCV, an open source computer vision library for image analysis and Tesseract OCR.

3. IMPLEMENTED MODEL

This project used data sets, SSD training in TensorFlow and Caffe frameworks, image pre-processing for OCR and tracking and motion functions.

3.1. Supervioned Learning

Supervised Learning (SL) is a kind of Machine Learning (ML) training in which the model is provided with labeled data during your training phase. The La- belImg tool was used, with PascalVOC format like the standard XML annotation (Figure 11), which includes class labels, coordinates of the bounding boxes, image path, image size and name and other tags.

Figure 11 – PascalVOC’s XML example Source: (EVERINGHAM et al.,2010)

3.1.1. Datasets Three sets were used: INRIA Person, Stanford Cars and GRAZ-02.

The Stanford Cars data set allowed SSD to identify cars along the quadcopter trajectory. This dataset contains 16,185 images from 196 classes of cars, divided into 8,144 training images and 8,041 test images, already noted in terms of make, model and year.

The INRIA person data set is a collections of digital images to highlight people, taken over a long period of time, and some web images taken from Google Images. About 2500 images were collected from that data set. The class of person was added because it was the most common false positive found in single-class training, along with a background class, to help improve the model discernment between different environments.

3.1.2. Caffe and TensorFlow Training Unlike TensorFlow, Caffe does not have a complete object detection API, which makes it more complicated when starting a new training. Caffe does not include a direct visualization tool like Tensorboard. However, it includes in its tools subdirectory, the parse log.py script, which extracts all relevant training information from the log file and makes it suitable for plotting. Using a Linux plotting tool called gnuplot, in addition to an analysis script, it was possible to build a real- time plotting algorithm (Figure 12). The latest log messages indicated that the model reached an overall mAP of 95.28%, which is an accuracy gain of 3.55% compared to the first trained model.

Figure 12 – Results from gnuplot custom script Source: (Author)

3.2. Image Preprocessing for ALPR

To help ALPR identify license plates, the quality of the images has been improved through the use of two techniques of image preprocessing brightness variation and sharp mask. The variation in brightness is controlled in the color system 16

used was RGB, where the color varies according to the levels of red, green and blue provided. There are 256 possible values for each level, ranging from 0 to 255. To change the brightness, just you need to add or subtract a constant value from each level. For brighter images, the value is add, while for darker images the values are subtracted, as seen in Figure 13, providing a total -255 to 255 values. The only necessary care is to check whenever the addition or subtracted value will be greater than 255 and less than 0.

Figure 13 – Effect of brightness on a colleted image Source: (Author)

After applying the brightness, the sharpness mask, also called the sharpness filter, was it is necessary to highlight the edges, thus improving the characters of the plate. Figure 14 shows an example of the result of this process.

Figure 14 – Effect of sharpness filter application Source: (Author)

3.3. Drone Control Algorithm

The algorithm for controlling the drone was built with the assistance of librar- ies in the Python language, which include OpenALPR, Caffe, TensorFlow, MAVSDK, OpenCV and others. Algorithm 1 is the main aogorithm of the solu- 17

tion and evaluates updated data from object detection and OCR at each speed on the x, y, z axes of the three-dimensional real-world environment.

3.3.1. Height Centralization Function Height centering is the only control function shared between views. It positions the drone at a standard altitude (Figure 14).

Figure 15 – Height centralization Source: (Author)

3.3.2. 2D-Centralization Function The drone has its camera pointed at the ground, the captured image is analo- gous to a 2D Cartesian system and the centralization is oriented using the x and y coordinates, as shown in Figure 16. The idea is to reduce the two Values ∆x and ∆y, which represent the detection x and y distances respectively central point (pink) to the central point of the frame (blue). 18

Figure 16 – 2D-centralization Source: (Author)

3.3.3. Yaw Alignment Function In the rear view approach, the alignment function is used to center the drone horizontally, according to the position of the car. The idea is to keep the drone fac- ing the car, as shown in Figure 17. These values are calculated using the a ∆yaw distance between the frame and the central detection point on the Y axis.

Figure 17 – Yaw alignment Source: (Author)

3.3.4. Approximation Function In the rear view approach, the zoom function was the most difficult to find, and used the distance from the object in the camera, the speed of the car and the balance between a safe distance from the car and the minimum distance for the OCR to work. The distance from the object was calculated using the relationship be- 19

tween the object camera field of view (FOV) and its sensor dimension, as seen in Figure 18.

Figure 18 – Object distance from camera Source: (Author)

4. EXPERIMENTS AND RESULTS

To evaluate the algorithm, a notebook was used as Ground Station, coordinat- ing the acquisition of the frame, the processing of the frame by the SSD, the values calculation and drone positioning command. Its specifications were an Ubuntu v18.04 64 OS, Intel R CoreTM i7 with 2.2 GHz, 16 GB of DDR3 SDRAM and an Nvidia GeForce GTX 1060 graphics card, with 6 GB of G-DDR5 memory.

4.1. The Simuled Drone

For the simulated experiment, a Typhoon H-480 model was used in the Gaze- bo, as shown in Figure 19. It is available in the PX4 Firmware Tools directory on Github, available at: https://github.com/PX4/Firmware/tree/master/Tools, ready to use on SITL environment. It was a handful, as it had a built-in gimbal and camera. The gimbal allowed the images to be captured in a very stable way, avoiding compromising the detections.

Figure 19 – Typhoon H-480 in Gazebo Source: (Author) 4.2. Simulated World

In the simulated experiments, a customized city (Figure 20) was created with precompilation models in the Gazebo model database, available at: https://bitbucket.org/osrf/gazebo models.

Figure 20 – Custom world in Gazebo Source: (Author)

4.3. CAT Vehicle

The CAT vehicle is a simulated distributed autonomous vehicle that is part of the ROS project in order to support research on autonomous driving technology. CAT has complete configurable simulated sensors and actuators imitating a real- world vehicle capable of autonomous driving, which includes a steering speed control implemented in real time simulation.

In the experiments the drone was adjusted some distance from the rear end of the car, as seen in Figure 21, and followed it, capturing its complete image and allowing the algorithm to process the recognized card. In addition, it allowed the plate to be personalized with a Brazilian model, making it very convenient for this project.

Figure 21 – The CAT Vehicle and Typhoon H-480 in Gazebo Source: (Author) 4.4. Simulated Experiments

The experiments related that in an urban scene, the most cars could be detected within a range of 0.5 to 4.5m from the camera, as shown in the green area in Fig- ure 22.

Figure 22 – Car detection simulation Source: (Author)

The detection range was 1.5 to 3 m. The balance between the main number of detections with the total of correct information extracted is represented in the green area of Figure 23.

Figure 23 – Plate detection and processing results Source: (Author) 22

Figure 24 shows a collision hazard zone represented by a red area.

Figure 24 – Car rear following results Source: (Author)

The ideal safety height and image quality vary between 4m and 6m. Figure 25 shows the red area where the height of other possible objects to be found, such as people and other vehicles, making it difficult to identify the moving vehicle as an object to be tracked.

Figure 25 – Car top following results Source: (Author)

4.5. The Real Quadrotor

A custom F450 (Figure 26) was used for the outdoor experiments. The challenge was to use cheap materials to achieve reasonable results.

The Pixhawk 4, the most expensive component, was the flight controller chosen board, as the idea was to use the PX4 flight stack as the control board configu- ration.

The numbers shown in Figure 26 are reference indexes for each of the components shown in Table 1. For the acquisition of the frame, an EasyCap capture device was connected to the computer, previously connected to the video receiver.

Figure 26 – Customized F450 built Source: (Author)

Table 1 – Customized F450 specifications

* Not added to weight sum, since it was used in the ground station ** The price is equivalent to USD 190,77 on March 08th, 2020

4.6. Outdoor Experiment

For the outdoor experiment on Campus (Figure 27), the model was changed to detect people instead of cars. To avoid colliding with the person who served as a target, everything was coordinated very slowly.

Another experiment used a video as images source (Figure 28), to check how many detections, plates and right information extracted the technique could ac- 24

quire. For the video, a record from Marginal Pinheiros was used, being one of the busiest highways in the city of São Paulo.

Figure 27 – Outdoor experiment in Campus Source: (Author)

Figure 28 – Experiment on record Source: (OLIVEIRA NA ESTRADA, 2018)

The experiment produced 379 vehicle detections of the 500 existing in a video clip, where 227 plaques were found, 164 with the correct information extracted (Figure 29).

Figure 29 – Experiment on record results Source: (author)

5. CONCLUSIONS AND FUTURE WORKS

In this project it was observed that a Single Class training for object detection models did not meet the needs of real time object detection for the experiments carried out. Multi Class allowed us to achieve good results.

The distance and the brightness level determined the limits in the tests performed, being an aspect to work on future improvements. A high definition camera should be used to prevent noise and vibrations in the captured images.

The mathematical functions used to calculate the drone's speed were useful in understanding the drone's behavior.

A different approach to position estimation or PID controller can be used to de- termine the object's route.

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