Abstract
Object detection is a field that has been in the limelight a lot in the recent years. Computer vision and image processing are involved in this computer technology and are widely used. Along the path of harnessing the power of vision, numerous algorithms have been found from simple edge detection to pixel-level object detection. In this paper, we have studied the advancements in object detection algorithms like R-CNN and the latest one being You-Only-Look-Once (YOLO). We have studied papers from 2016 to 2018 based on the types of R-CNN like Fast R-CNN, Faster R-CNN, and Mask R-CNN and various versions of YOLO. We have seen their applications in various fields, studied their efficiency, accuracy, and limitations.
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Desai, K., Parikh, S., Patel, K., Bide, P., Ghane, S. (2020). Survey of Object Detection Algorithms and Techniques. In: Gunjan, V., Suganthan, P., Haase, J., Kumar, A., Raman, B. (eds) Cybernetics, Cognition and Machine Learning Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1632-0_23
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DOI: https://doi.org/10.1007/978-981-15-1632-0_23
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