Abstract
Computer vision is the field of science that studies how computers and software can recognise and understand images and scenes. Computer vision has many different parts, such as recognising images, finding objects, making images, making images bigger, and more. Object detection in real time is a big, busy, and hard area of computer vision. Object detection is when more than one object needs to be found in an image. Image localization is when there is only one object to find in an image. This finds the objects of a class that make sense in digital pictures and videos. Real-time object detection can be used for many things, such as tracking objects, video surveillance, recognising pedestrians, counting people, self-driving cars, recognising faces, following a ball in sports, and many more. As deep learning grows quickly, more powerful tools that can learn semantic, high-level, and deeper features are made available to solve problems in traditional architectures. The network architecture, training strategy, and optimization function of these models are all different. Convolution neural network is an example of a deep learning tool that can be used to find objects with OpenCV, which is a library of programming functions that are mostly used for real-time computer vision. Most accidents happen because of things that get in the way, like other people, cars, people, fire hydrants, traffic signs, and so on. This is mostly about finding these kinds of problems that can lead to disaster.
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Rajesh Kumar, G. et al. (2023). Object Detection Using Deep Learning Approaches. In: Seetha, M., Peddoju, S.K., Pendyala, V., Chakravarthy, V.V.S.S.S. (eds) Intelligent Computing and Communication. ICICC 2022. Advances in Intelligent Systems and Computing, vol 1447. Springer, Singapore. https://doi.org/10.1007/978-981-99-1588-0_63
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