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Real-Time Vehicle Make and Model Recognition with the Residual Squeezenet Architecture

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Real-Time Vehicle Make and Model Recognition with the Residual Squeezenet Architecture sensors Article Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture Hyo Jong Lee 1,* , Ihsan Ullah 2, Weiguo Wan 1, Yongbin Gao 3 and Zhijun Fang 3 1 Division of Computer Science and Engineering, CAIIT, Chonbuk National University, Jeonju 54896, Korea; [email protected] 2 Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea; [email protected] 3 School of Electrics and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; [email protected] (Y.G.); [email protected] (Z.F.) * Correspondence: [email protected]; Tel.: +82-63-270-2407 Received: 20 December 2018; Accepted: 21 February 2019; Published: 26 February 2019 Abstract: Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications. Keywords: vehicle make recognition; deep learning; residual SqueezeNet 1. Introduction In recent years, a plethora of innovative technologies and solutions are bringing intelligent transportation systems (ITSs) closer to reality. ITSs are advanced transportation systems that aim to advance and automate the operation and management of transport systems, thereby improving upon the efficiency and safety of transport. ITSs combine cutting-edge technology such as electronic control and communications with means and facilities of transportation [1]. The development of digital image processing and computer vision techniques offers many advantages in enabling many important ITSs applications and components such as advanced driver-assistance systems (ADASs), automated vehicular surveillance (AVS), traffic and activity monitoring, traffic behavior analysis, traffic management, among others. Vehicle make and model recognition (MMR) is of great interest in these applications, owing to heightened security concerns in ITSs. Over the years, numerous studies have been conducted to solve different challenges in vehicle detection, identification, and tracking. However, classification of vehicles into fine categories has gained attention only recently, and many challenges remain to be addressed [2–5]. The traditional vehicle MMR system relies on manual human observation or automated license plate recognition (ALPR) technique, these indirect progresses make the vehicle MMR system hardly meets the real-time constraints. Through manual observation, it is practically difficult to remember and efficiently distinguish between the wide variety of vehicle makes and models; it becomes a laborious and time-consuming task for a human observer to monitor and observe the multitude of screens and record the incoming or outgoing makes and models or to even spot the make and model being looked Sensors 2019, 19, 982; doi:10.3390/s19050982 www.mdpi.com/journal/sensors Sensors 2019, 19, 982 2 of 14 Sensors 2019, 19, x FOR PEER REVIEW 2 of 14 for. The MMR systems that rely on license plates may suffer from forging, damage, occlusion, etc., asshown shown in inFigure Figure 1.1 In. In addition, addition, there there are are some some license-plates license-plates that that can can be ambiguousambiguous inin termsterms ofof interpreting lettersletters (e.g.,(e.g., between between “0” “0” and and “O”) “O”) or or license license types. types. Moreover, Moreover, in in some some areas, areas, it mayit may not not be requiredbe required to bearto bear the the license license plate plate at the at frontthe front or the or rear. the rear. If the If ALPR the ALPR system system is not is equipped not equipped to check to forcheck license for license plates plates at both at (front both and(front rear) and ends rear) of ends the vehicle,of the vehicle, it could it fail.could Consequently, fail. Consequently, when ALPRwhen systemsALPR systems fail to correctly fail to correctly read the detectedread the licensedetected plates license due plates to the abovedue to issues, the above the wrong issues, make-model the wrong informationmake-model couldinformation be retrieved could frombe retrieved the license-plates from the license-plates registry or database. registry or database. (a) Ambiguous (b) Forged (c) Damaged (d) Duplicated Figure 1. SomSomee cases that cause failure of the licenselicense plate recognition-based MMR systems. To overcomeovercome thethe aforementionedaforementioned shortcomingsshortcomings in traditional vehicle identificationidentification systems, automated vehicle MMR techniques have becomebecome critical.critical. The The make make and and model of the vehicle recognized by by the the MMR MMR system system can can be be cross-checked cross-checked with with the the license-plate license-plate registry registry to check to check for any for anyfraud. fraud. In this In thisdual dual secure secure way, way, vision-based vision-based automated automated MMR MMR techniques techniques can can augment augment traditional ALPR-based vehicle classificationclassification systemssystems toto furtherfurther enhanceenhance security.security. Abdel Maseeh et al. [[6]6] proposed an approach to addressaddress thisthis specificspecific problemproblem byby combiningcombining global and local information and utilizing discriminative information labelled labelled by by a a human human expert. expert. They validated their approach throughthrough experiments on recognizing the make and model of sedan cars from single single view view images. images. Jang Jang and and Turk Turk [7] [7 demonstrated] demonstrated a car a car recognition recognition application application based based on onthe theSURF SURF feature feature descriptor descriptor algorithm, algorithm, which which fuses fuses bag-of-words bag-of-words and and structural verificationverification techniques. Baran Baran et et al. al. [8] [8] presented a smart cameracamera in intelligent transportation systems for the surveillance of vehicles. The The smart smart camera camera can can be used for MMR, ALPR, and color recognition of vehicles. In In [9], [9], Santos Santos et et al. al. introduced introduced two two car car recognition recognition methods, methods, both both relying relying on on the the analysis analysis of ofthe the external external features features of the of the car. car. The The first first method method evaluates evaluates the the shape shape of the of the car’s car’s rear, rear, exploring exploring its itsdimensions dimensions and and edges, edges, while while the the second second considers considers features features computed computed from from the the car’s car’s rear rear lights, notably, their their orientation, orientation, eccentricity, eccentricity, position, position, angle angle to the to thecar carlicense license plate, plate, and shape and shape contour. contour. Both Bothmethods methods are combined are combined in the in proposed the proposed automatic automatic car carrecognition recognition system. system. Ren Ren et etal. al.[10] [10 proposed] proposed a aframework framework toto detect detect a amoving moving vehicle’s vehicle’s make make an andd model model using convolutional neural networks (CNNs). Dehghan Dehghan et et al. al. [11] [11] proposed a CNN-basedCNN-based vehicle make, model, and color recognitionrecognition system, which is computationally inexpensive and provides state-of-the-art results. Huang et al. [[12]12] investigated fine-grained fine-grained vehicle recognition using deep CNNs. They localized the vehicle and the corresponding parts with the help of region-basedregion-based CNNs (RCNNs) and aggregated their features from a set of pre-trained CNNs to train a support vector machine (SVM) classifier.classifier. Gao and Lee [[13]13] proposed aa locallocal tiled tiled CNN CNN (LTCNN) (LTCNN) strategy strategy to alterto alter the the weight-sharing weight-sharing scheme scheme of CNN of CNN with with a local a tiledlocal structure,tiled structure, which which can provide can provide the translational, the translational, rotational, rotational and scale, and invariancescale invariance for MMR. for MMR. In this study, wewe focusfocus onon addressingaddressing thethe challengeschallenges inin realreal timetime andand automatedautomated vehiclevehicle MMR by utilizingutilizing state-of-the-artstate-of-the-art deepdeep learning-basedlearning-based techniques.techniques. Vanilla Vanilla CNNs have demonstrateddemonstrated impressive performance in vehiclevehicle MMR-related tasks. However, However, long training periods and large memory requirementsrequirements in in deployment deployment environments environments often often constrain constrain the the use use of suchof such models models in real-time in real- applicationstime applications as well as well as distributed as distributed environments. environments. Thus, Thus, in in this this study, study, a a smaller smaller CNNCNN architecture named SqueezeNet
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