Object Detection Method Based on YOLOv3 using Deep Learning Networks
A. Vidyavani1, K. Dheeraj2, M. Rama Mohan Reddy3, KH. Naveen Kumar4
1A. Vidyavani , Department of Computer Science and Engineering, RM Institute of Science and Technology, Chennai
2K. Dheeraj, Department of Computer Science and Engineering, RM Institute of Science and Technology, Chennai
3M. Rama Mohan Reddy, Department of Computer Science and Engineering, RM Institute of Science and Technology, Chennai
4KH. Naveen Kumar, Department of Computer Science and Engineering, RM Institute of Science and Technology, Chennai
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1414-1417 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4121119119/2019©BEIESP | DOI: 10.35940/ijitee.A4121.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: (YOLOv3, Deeplearning, Dimensional Clustering, Object Detection)
Scope of the Article: Clustering