When we watch a movie or look through a collection of photographs, we can instantly understand what they are just by looking at them; this is an intrinsic capacity that humans have gradually gained as part of growth. Object detection is a sophisticated technology that can perform the same thing. It may sound futuristic, yet it is happening right now.
Object detection is a technique of the Artificial Intelligence (AI) subset, computer vision, that is concerned with identifying objects and categorizing them as persons, cars, animals, and so on.
Object detection seeks to create computational models that offer the most basic information required by Computer Vision applications: "What things are where?"
Object Detection and Deep Learning
The rapid advancement of deep learning algorithms in recent years has substantially accelerated the momentum of object detection. Deep learning networks and GPU computing power have considerably improved the performance of object detectors and trackers, resulting in significant breakthroughs in object detection.
Machine Learning (ML) is a subfield of AI that entails learning patterns from examples or sample data as the machine accesses and learns from it (supervised learning on annotated images). Deep Learning is a subset of ML that incorporates learning at several stages.
Object Detection Use Cases and Applications
The use cases for object detection are numerous; there are nearly limitless ways to let computers see like humans in order to automate manual jobs or create new, AI-powered goods and services. It has been utilized in computer vision programs for a variety of purposes ranging from sports production to productivity analytics.
Object detection is now at the heart of most vision-based AI software and algorithms. Object detection is essential for scene interpretation, which is useful in security, transportation, medical, and military applications.
Object Detection in Retail
People counting systems strategically placed throughout numerous retail locations are used to collect information about how customers spend their time and footfall. This became extremely useful ever since the pandemic. It is vital to control and monitor the number of customers in a particular store. Besides that, AI-based customer analysis using cameras to recognize and monitor customers helps to obtain a better knowledge of customer interaction and experience, enhance store layout, and make operations more effective. A common use is the identification of lineups in order to reduce waiting time in retail outlets.
Autonomous Driving
Object detection is used by self-driving automobiles to distinguish pedestrians, traffic signs, other vehicles, and other objects. Tesla's Autopilot AI, for example, largely relies on object detection to detect environmental and surrounding risks such as impending vehicles or barriers.
Animal Detection in Agriculture
Object detection is utilized in agriculture for tasks such as counting, animal monitoring, and product quality evaluation. ML algorithms can detect damaged produce while it is being processed. This does not only save ample time but also ensures products are of high quality.
Human Detection in Security
Object detection is used in a variety of security applications in video surveillance, such as detecting persons in restricted or dangerous areas, preventing suicide, and automating inspection chores on remote locations using computer vision.
Vehicle Detection in Transportation
Object detection is used to recognize and count vehicles for traffic analysis or to detect cars that halt in unsafe situations, such as on motorways or junctions. Apart from vehicle detection, license plate recognition also uses object detection. This is useful to retrieve information about a certain vehicle. As part of one of our projects, Ever AI Technologies created our own LPR system. Following the identification of the license plate number, vital information such as the vehicle type, manufacturer, production year, and road tax validity can be obtained.
Object Detection in Healthcare
Many medical breakthroughs have been made possible by object detection. Because medical diagnostics rely largely on the analysis of pictures, scans, and photographs, object detection using CT and MRI scans has proven incredibly effective for diagnosing diseases, such as with ML algorithms for tumor detection.
Conclusion
Most computer and robot vision systems rely heavily on object detection. Although significant progress has been made in recent years, and some existing techniques are now included in many consumer electronics (e.g., face detection for auto-focus in smartphones) or have been integrated into assistant driving technologies, we are still a long way from achieving human-level performance, particularly in open-world learning.
Object detection can help automate certain processes, giving retail, healthcare, manufacturing, transportation, and other industries a competitive advantage. By integrating smart software platforms, Exposit Machine Learning (ML) developers have increased their knowledge in addressing complicated business tasks. If you wish to turn your company idea into an effective computer vision solution, please contact us.
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