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http://dx.doi.org/10.7471/ikeee.2021.25.4.750

Improving the Vehicle Damage Detection Model using YOLOv4  

Jeon, Jong Won (Dept. of Information and Communications Engineering, Hankuk University of Foreign Studies)
Lee, Hyo Seop (Dept. of Information and Communications Engineering, Hankuk University of Foreign Studies)
Hahn, Hee Il (Dept. of Information and Communications Engineering, Hankuk University of Foreign Studies)
Publication Information
Journal of IKEEE / v.25, no.4, 2021 , pp. 750-755 More about this Journal
Abstract
This paper proposes techniques for detecting the damage status of each part of a vehicle using YOLOv4. The proposed algorithm learns the parts and their damages of the vehicle through YOLOv4, extracts the coordinate information of the detected bounding boxes, and applies the algorithm to determine the relationship between the damage and the vehicle part to derive the damage status for each part. In addition, the technique using VGGNet, the technique using image segmentation and U-Net model, and Weproove.AI deep learning model, etc. are included for objectivity of performance comparison. Through this, the performance of the proposed algorithm is compared and evaluated, and a method to improve the detection model is proposed.
Keywords
Deep Learning; YOLOv4; Object Detection; Computer Vision; Vehicle Damage Detection;
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