What is Computer Vision?
Computer vision is a branch of artificial intelligence (AI) that enables computers to see and recognize images, processing them as humans would. As you use images from videos and cameras, deep learning models allow machines to recognize and classify objects with accuracy.
Comprehensively, computer vision refers to the science and technology that aims at building artificial systems. These systems capture information from multi-dimensional images. It emphasizes on replicating components of the human vision system complexities to enable computers to identify and process images and videos. This is possible due to innovations in deep learning as well as neural networks that have helped AI make huge leaps.
As one of the hottest and the most trending research fields within deep learning currently, computer vision sits at the intersection of several academic subjects, like computer science (includes graphics, theory, algorithms, systems, architecture), mathematics (includes machine learning, information retrieval), physics (includes optics), engineering (speech, robotics, image processing, natural language processing), psychology (includes cognitive science), and biology (includes neuroscience). Computer vision exemplifies a relative understanding of visual environments and their contexts. Scientists thus believe the field of computer vision paves the way for artificial intelligence.
Computer Vision and Machine Learning
Artificial Intelligence (AI) has been gaining a lot of interest for decades now. After all, technology never stops trying to copy the human brain. Discussing the relationship between AI, computer vision, and machine learning, it is worth noting that AI is the umbrella of all of these fields. Machine learning (ML) is a subset of AI and computer vision is a subset of machine learning. Computer vision can be understood as a direct subset of AI.
The fields of computer vision and machine learning have become closely linked to one another. Machine learning has improved computer vision in terms of image recognition and tracking. It provides effective methods for the acquisition, image processing, and object focus used in computer vision. We can say that computer vision has widened the scope of machine learning. Computer vision encompasses a digital image or video, an interpreting device, a sensing device, and the stage of interpretation. ML is used in computer vision in the interpreting device and at the interpretation stage.
Machine learning is a broader field, evident in the algorithms that can be applied to other fields. An example here can be the analysis of a digital recording implemented using ML principles, while computer vision mainly deals with digital images and videos. It has relationships in the fields of information engineering, neurobiology, physics, and signal processing.
How Does Computer Vision Work?
Computer vision can often be confused with image processing. Computer vision, however, is a more high-level process. It deals with analyzing an image. In the process of computer vision, the input is an image, while the output is the interpretation of that image. This science works by recognizing and analyzing different components within the image.
A simple application detects the edges in an image. It is the computer vision that enables shape identification or pattern recognition. And then there are highly advanced applications that allow people or animals to be identified. This application of computer vision is core to drones and automated vehicles, including facial recognition and augmented reality software. Computer vision, AI, and machine learning concepts are central to realizing complete, or Level 5 automation in self-driving vehicles. In this case, CV software analyzes data from cameras positioned around the car. The car thus detects pedestrians and other vehicles and also reads road signs.
Computer vision also enables the development of accurate facial recognition software, which is regularly executed by law enforcement agencies and even helps in authenticating consumer device ownership. Whether they are mixed and augmented reality technologies used in tablets and smartphones, or wide use of smart glasses, CV aids in determining the location, identifying objects, and establishing the depth or dimensions of the virtual world.
Benefits of Computer Vision
It’s quite important to understand how computer vision systems are advantageous to business users. With the ability to see and interpret, computer vision applications automate various tasks without any need for human intervention. Businesses can be benefitted by:
- Makes Process Simple & Fast
Computer vision applications can perform monotonous, repetitive tasks faster, thus, making the entire process of protracted visual checks simpler.
Contrary to a human eye, computers and cameras don’t get tired. As the human factor is eliminated, you don’t notice any changes in reliability, for example, how controllers slept that day, or what day of the week it was.
It is a truth that machines rarely make mistakes. Computer vision systems with image-processing capabilities, thus, are expected to commit zero mistakes. As a result, products or services offered will be faster and of high quality.
- Cost Reduction
As machines take on the responsibility of performing burdensome tasks, companies can save on a lot of money with minimization of errors, leaving no room for faulty services or products. After all, your business doesn’t want to spend on fixing erroneous products and processes. Your employees can shift their focus from the control department to other activities and you can even improve your image amongst customers by mitigating the error rate in deliveries.
- Wide Usage
We can see the same computer vision system in different fields and activities. Right from warehouse tracking in factories to shipping of supplies, and to scanning of images in the medical industry, computer vision has a wide range of applications.
We shall now discuss applications of computer vision systems in different industries.
The Automotive Industry
Right from the vehicle manufacturing process and assembly line to vehicles on streets, computer vision has been making driving safer with every passing day.
Advanced Driver Assistance Systems (ADAS) Applications: Applications of computer vision in Advanced Driver Assistance Systems are prominent. ADAS applications comprise night vision assist, drowsy driver detection, lane departure warning, pedestrian and traffic sign recognition, forward collision warning, and blind-spot monitoring. Vision-based ADAS, RADAR, and LIDAR technologies under computer vision systems are laying the groundwork for automotive businesses towards coming up with fully automated self-driving cars. Different systems inside a car perform varied tasks, such as camera-based ADAS offers visual representation, RADAR works when there is low visibility, and LIDAR provides a 3D representation of the surroundings of a vehicle with object recognition.
Remember all these applications of Advanced Driver Assistance Systems become possible with computer vision technologies, and they together offer a holistic solution. The driver can thus gain a better awareness of his surroundings while driving and exercising better control than before.
Automotive Gesture Recognition with computer vision is emerging as the next level in road safety, and this is based on machine learning and deep learning capabilities. This technology observes the facial and hand gestures of the driver, notifying if they differ from the pre-programmed recognizable gestures. Facial expressions, such as drowsiness, eye blinking, and head movements are identified with computer vision technologies. The driver gets notified via visual or audible alerts.
While we’re on the topic of the safety and security management of drivers, passengers, and goods, computer vision also aids in managing transportation and fleets. Supported by automotive cameras and telematics solutions, vision-based solutions play an important role in telematics, besides managing fleet operations seamlessly.
Talking about fleet management, computer vision guarantees safety and security of the vehicles in transit, driver, and passengers, as well as consignment, while optimizing fleet routes. Some of the ways in which computer vision applications help to improve fleet operations include observing driver’s behavior, incident capture, consignment tracking, route optimization, number plate recognition, and time management.
The Retail Industry
Computer vision algorithms help in improving retail store security with advanced video analytics, such as face and motion detection, queue management, people counting, gesture recognition, trespassing detection, and vandalism.
Besides guaranteeing security, theft control, and spillage detection, video analytics with computer vision and AI emphasizes more on improving the shopping experience of customers as well as optimizing operations. Retail stores with machine learning and computer vision capabilities deployed at customer touchpoints gather customers’ data as well as process information about product placement, customer needs, and in-shelf product inventory.
Computer vision, with the help of cameras on shelves and in-store cameras, captures images of the products in the aisles and processes those images to draw actionable insights. You can thus digitize every single detail of the products in aisles, including shelf share, assortments, new products launched, product availability, brands, and variants, pricing and discounts, as well as out of stock products.
Besides, deep learning algorithms and vision-based technologies aid in generating product insights, for instance, if products are placed in the proper place, product promotion, the effect of product placement on its sales, and creating brand awareness. The latest trend in the retail industry is applying computer vision to offer end-to-end contactless solutions and minimize checkout time for their customers. Computer vision with deep learning algorithms offers solutions to identify customers and their chosen products for easy checkout with electronic point of sale (EPOS) integration and object detection.
The Manufacturing Industry
Production lines in high-tech manufacturing units are quite complex and automated. Computer vision technologies in manufacturing units are very useful with unparalleled business benefits such as:
Predictive Maintenance: Modern manufacturing units are equipped with costlier automated production lines featuring conveyer units and robotics, and manufacturing businesses cannot afford to have an unanticipated breakdown of any part of the system. A mild breakdown in the production line, owing to the defect in the system, can be quite disastrous for the business. This is where the role of computer vision becomes prominent in analyzing every component of the production line and diagnosing even the little defects in the system. Based on the accurate as well as a detailed investigation, computer vision systems can well predict any chances of future failure in the system, notify the technical team to fix the issue, and ensure that there is no downtime in the production.
Defect Identification: Inspection for the defects in the industrial setup can be very tedious, time-consuming, risky, and costly. Besides, it sometimes becomes next to impossible to detect any defects in the machines manually. In such cases, computer vision helps eliminate risks for workers. Computer vision systems are accurate enough to recognize cracks, leaks, corrosion, and other anomalies in the machines. They are known for anomaly detection.
Product Quality Inspection: In traditional manufacturing units, product quality inspection is the last step in the production cycle. With the introduction of computer vision supported by ML and AI techniques, the quality of products, along with their packaging, are inspected at every stage of production with high accuracy and precision. This leads to minimal waste of the product, efforts, and most importantly, cost.
The Security and Surveillance Industry
The security and surveillance industry is one of the early adopters of computer vision systems. These systems have greatly improved the precision and techniques in intelligent video analytics and video surveillance. The volume of data that video surveillance systems generate depends on the number and types of video cameras as well as their resolution for specific projects. Computer vision makes it possible to generate crucial information from the many feeds by using ML and AI in video analytics.
Computer vision capabilities in the field of security and surveillance are based on video management software and its hardware, network interfaces, third party devices that include sensors, access control devices and alarms, signal processing, and pattern and object recognition.
City Surveillance: Computer vision applications have made the concept of the smart city possible. Monitoring of public places (bus terminals, parking lots, hospitals, railway stations, subways, highways, traffic junctions, and cross-roads) with advanced video analytics, driven by computer vision algorithms, ensures their security. A few of the vision-based applications in city surveillance comprise of crowd detection, camera tampering, face recognition/capture, number plate recognition, vehicle tracking, tailgating, missing/left object detection, speeding vehicles, illegal parking, and vehicle wrong way detection.
Are you leveraging computer vision applications?
The computer vision industry has a broader scope and computer vision applications are not confined to the industries discussed above. Computer vision can be applied in other industries, like life sciences, oil, and gas, consumer electronics, agriculture, and sports. The reason behind its adaptability in almost every industry is its precision and accuracy. If designed effectively for any use case, computer vision algorithms help to save time, trim down operating costs, and improve productivity. If you are looking to ensure your workers’ safety leveraging technology, click here to learn more.