Object Detection Method Based on YOLOv3 using Deep Learning Networks

• Published in: International journal of innovative technology and exploring engineering 2019-11, Vol.9 (1), p.1414-1417
• Main Authors: Vidyavani, A., Dheeraj, K., Reddy, M. Rama Mohan, Kumar, KH. Naveen
• Format: Article
• Language: English
• ISSN: 2278-3075; 2278-3075
• DOI: 10.35940/ijitee.A4121.119119

Object Detection is being widely used in the industry right now. It is the method of detection and shaping real-world objects. Even though there exist many detection methods, the accuracy, rapidity, and efficiency of detection are not good enough. So, this paper demonstrates real-time detection using the YOLOv3 algorithm by deep learning techniques. It first makes expectations crosswise over 3 unique scales. The identification layer is utilized to make recognition at highlight maps of three distinct sizes, having strides 32, 16, 8 individually. This implies, with partner contribution of 416 x 416, we will in general form location on scales 13 x 13, 26 x 26 and 52x 52. Meanwhile, it also makes use of strategic relapse to anticipate the jumping box article score, the paired cross-entropy misfortune is utilized to foresee the classes that the bounding box may contain, the certainty is determined and afterward the forecast. It results in perform multi-label classification for objects detected in images, the average preciseness for tiny objects improved, it's higher than quicker RCNN. MAP increased significantly. As MAP increased localization errors decreased.