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Machine Vision and Industrial Automation Accelerating Adoption in Industrial Automation Jayavardhana Gubbi Jayavardhana P Balamuralidhar

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Machine Vision and Industrial Automation Accelerating Adoption in Industrial Automation Jayavardhana Gubbi Jayavardhana P Balamuralidhar Machine Vision and Industrial Automation Accelerating Adoption in Industrial Automation Jayavardhana Gubbi Jayavardhana P Balamuralidhar Machine vision has harnessed deep learning admirably. Disruptive solutions are in the market for security and surveillance, autonomous vehicles, industrial inspection, digital twins, and augmented reality (AR). However, historically, data acquisition within computer vision has been hamstrung by manual interpretation of captured image data. Fortunately though, deep learning has generated data-driven models that could replace handcrafted preprocessing and feature extraction superbly. IN BRIEF The future has already arrived. TCS has, for instance, not only delivered revolutionary deep learning-powered machine vision solutions, but is also using advanced software algorithms to design industry-grade spatial cognition-enabled computer vision applications. A field established in the 1960s, Machine vision today it’s only in the past 30 months or so that the machine vision Key technological developments in industry has harnessed deep miniaturized cameras, embedded learning—with extremely devices, and machine learning good results. Poised where AI, have historically enabled adoption imaging sensors, and image of machine vision in a multitude processing meet, machine vision of applications—satellite image is now making a massive impact in analysis and medical image industrial applications. interpretation, among others— across diverse domains. This has brought disruptive industry-grade solutions to the But the game-changer today is market. Table 11 sets out the key the power and affordability of industry sectors in which computer computing devices, including vision has made inroads. the advent of digital and mobile cameras. This has resulted in image The key application trends processing spreading to other bringing machine vision to the fore areas and making humongous include security and surveillance, volumes of real (as against autonomous vehicles, industrial synthetic) visual data available inspection, digital twins, and AR. for testing. Reimagining Research 2018 Data_Section_TCS-Checked0315.indd 26 29/01/1941 Saka 15:27 Fact File TCS Research: Machine Vision Outcomes: Vision for Drones, Warehouse Inventory Management, Insurance and Utilities use cases, Forestry, xVision Principal Investigator: Jayavardhana Gubbi Academic Partners: IISc, Bengaluru, IIIT Hyderabad Techniques used: Machine Learning, Computer Vision, and Pattern Recognition Industries benefited: Multiple industries where visual inspection and automation are an integral part Patents: 27 Papers: 28 publications Maturity of Current Level of Market Example Size of Solutions Market Sophistication Segment Applications Market and API Uptake Needed Availability Augmented reality (AR) Extremely Early-stage Consumer and virtual reality (VR) Improving Low large availability retail analytics Security and Perimeter monitoring, Mature and Large High Medium surveillance theft detection available Strategy in Early-stage Entertainment sports, intelligent Small, niche Low Medium availability advertisements Early disease detection, tumor detection, Medium, Medical Mature Medium High tracking, and gated niche therapy Advanced driver assistance systems Automotive (ADAS), lane detection, Very large Mature Low Very high and autonomous driving Navigation and Very large, Early-stage Robotics Low Very high perception of the world niche availability Table 1: Major sectors contributing to the computer vision boom However, in most applications, the environmental conditions such as data acquisition process is saddled lighting or illumination, contrast, with manual interpretation of the quality, resolution, noise, scale, captured image data. Variations in pose (of the object), and occlusion Machine Vision and Industrial Automation Data_Section_TCS-Checked0315.indd 27 29/01/1941 Saka 15:27 (blocking of part of the field of But the good news is that the view) necessitate human-based emergence of deep learning The good news is preprocessing and handcrafting has resulted in generating data- for shadow removal, illumination driven models that could replace that the emergence normalization, and so on. These are handcrafting operations, such of deep learning has among the most time-consuming as preprocessing and feature resulted in generating tasks in enabling the machine extraction, with extremely good to make holistic sense of the results. Further, the deep learning datadriven models collected data. approach exploits an emerging that could replace area within AI—algorithm This limitation becomes severe development for unsupervised handcrafting when one considers that modern machine learning—called operations. digital cameras, generating vast generative adversarial networks amounts of still and video-image (GANs), which allow data synthesis data—together with wireless to cater to variations, while technology—which enables their enabling the design of more robust easy deployment, makes imperative algorithms.2 These allow reuse of the processing of zettabytes (trillion models in different applications, gigabytes) of data. (In fact, this is through transfer learning. the order of magnitude of the visual data already being collected by the Let us look at some technologies Internet of Things (IoT) devices in businesses are employing today: smart cities!) Artificial Intelligence Pervasive, XR (Augmented Reality, Virtual Scale-, pose-, and occlusion- Reality, Mixed Reality) and domain invariant image processing are still based digital platforms. In these, in their infancy. Object detection more than 50% of the world is still the first phase in image involves computer vision as a processing, where an image is first fundamental horizontal step. For preprocessed, followed by feature instance, autonomous vehicles extraction (identification and and drones under AI Everywhere; tagging of image features, such as Augmented reality and human the shape, size, and color of areas augmentation in XR; and Digital within it) and evaluation. These twin in digital platforms use evaluated features are then used in computer vision at some stage a machine learning framework for of processing. Further, these obtaining the final result. In addition technologies are spread in near to classification, other necessary term as well as long term reflecting image processing operations, the complexity of task that will have such as image registration, image to be addressed in machine vision. segmentation, and image stitching, are also addressed. For identification of non-rigid objects, deformable models are normally employed. Reimagining Research 2018 Data_Section_TCS-Checked0315.indd 28 29/01/1941 Saka 15:27 Using drones for warehouse inventory monitoring In million+ Stock Keeping Unit (SKU) warehouses taking an inventory count is an elaborate manual affair, where workers need to climb tall ladders to check labels. TCS has built a drone-based solution, which has been carrying out inventory counting at large (500,000-1,000,000 SKU) warehouses worldwide. Designed and implemented by the Machine Vision Group and the Drones Incubation team at TCS Research, the solution programs commercial off-the-shelf drones to autonomously navigate 50-100 racks, each 20-50 ft. tall, across 400-500 ft. aisles. The total time taken for a drone to complete such a mission is around 20 minutes. Inventory counting is over 98% accurate, which matches human accuracy but gets the job done more consistently and much faster. Minimal manual intervention is required from a human inventory auditor, who just taps a tablet touchscreen for the drone to take off, fly, hover, record, report, and land. With a few more taps, the auditor specifies the number of racks and shelves to cover for a given mission. Each tablet app controls multiple drones simultaneously. On mission completion, the drone finds its way back to its base station, lands safely, and powers off. Each drone is programmed to make sense of the dimensions, floor plan, and layout of the warehouse. The drones use this data to calculate mission range. Inbuilt fail-safe features include obstacle avoidance (collision prevention with humans, inventory, or warehouse structural projections), gyrostabilization (when subjected to unexpected drafts of air or to rotor malfunction), nearest permissible emergency safe landing location determination, and systematic shutdown without data loss. On mission completion, the inventory data, hosted on a private cloud, becomes immediately retrievable. Integrating the drone-based solution with the warehouse management system—a one-time, 4-person-hour investment—enables report generation on the fly, with full data visibility. The architecture of a The cameras must be capable machine vision system of recording across the entire electromagnetic spectrum (the A modern-day computer vision visible 380–760 nm/400–790 system comprises five modular terahertz and nonvisible 1 architectural elements (Figure 1). picometer–1,000 km/3 Hz–1 million terahertz range). Thus, Image acquisition depending on the application, This constitutes cameras mounted image acquisition may contain on drones, mobile phones, and LIDAR, hyperspectral, multispectral, other devices that map real- thermal, and ultraviolet cameras, world 3D data to a 2D matrix. apart from visual cameras. Machine Vision and Industrial Automation Data_Section_TCS-Checked0315.indd 29 29/01/1941 Saka 15:27 IMAGE ACQUISITION DATA STORE IMAGE ENHANCEMENT SCENE INTERPRETATION UNDERSTANDING AND VISUALIZATION Visual
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