Abstract
Object detection is considered as a challenging field in computer vision. Once an object has been detected, the next challenge is object localization where a rectangular boundary box is drawn around the location of detected object. The proposed framework addresses the problem of object localization by improving its precision. You only look once or YOLOv3 is one of the well-known object detection algorithm with its state-of-the-art object detection and real time capabilities. Because of this reason, the proposed scheme uses YOLOv3 as the base algorithm. In this work, COCO dataset is used to detect an object, and to improve the precision of boundary box this work make use of edge detection, thresholding and morphological operation. Also, redundant edge removal algorithm is proposed to remove redundant edges and boundary box construction algorithm draws rectangular boundary box around detected object. When compared with YOLOv3, the proposed model produces significantly better results when boundary boxes around detected object is concern.
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Blue, S.T., Brindha, M. (2020). An Edge Detection and Sliding Window Based Approach to Improve Object Localization in YOLOv3. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1240. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_13
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