关键词: 目标检测, YOLO V3, 聚类算法, 损失函数

Abstract:

Aiming at the problems of poor performance and high missed detection rate when the general target detection algorithm detects small targets, a road small target detection algorithm based on improved YOLO V3 is proposed. The clustering algorithm in the original YOLO V3 algorithm network model is optimized, the DBSCAN+K-Means clustering algorithm is used to cluster the training dataset, more appropriate Anchor Boxs are selected to improve the detection average precision and speed. At the same time, the Focal Loss loss function is introduced to replace the loss function in the original network model to form an improved YOLO V3 algorithm. Compared with other target detection algorithms on the KITTI dataset for person detection, it is found that the improved YOLO V3 algorithm can effectively reduce the missed detection rate of small target detection, and greatly improve the average precision and detection speed. Experimental results show that, on the KITTI dataset, the average precision of the improved YOLO V3 algorithm reaches 92.43%, which is 2.36% higher than the unimproved YOLO V3 algorithm, and the detection speed reaches 44.52 frames per second.

Key words: target detection, YOLO V3, clustering algorithm, loss function