Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (21): 218-223.DOI: 10.3778/j.issn.1002-8331.2005-0340

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Road Small Target Detection Algorithm Based on Improved YOLO V3

YUE Xiaoxin, JIA Junxia, CHEN Xidong, LI Guang’an   

  1. 1.School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
    2.School of Tourism, Lanzhou University of Arts and Science, Lanzhou 730030, China
  • Online:2020-11-01 Published:2020-11-03

改进YOLO V3的道路小目标检测


  1. 1.兰州交通大学 电子与信息工程学院,兰州 730070
    2.兰州文理学院 旅游学院,兰州 730030


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


针对通用目标检测算法在检测小目标时存在效果不佳及漏检率较高等问题,提出了一种基于改进YOLO V3的道路小目标检测算法。对YOLO V3算法网络模型中的聚类算法进行优化,使用DBSCAN+K-Means聚类算法对训练数据集聚类分析,选取更合适的Anchor Box,以提高检测的平均精度和速度;同时引入Focal Loss损失函数代替原网络模型中的损失函数形成改进的YOLO V3算法。进而与其他目标检测算法在KITTI数据集上对行人目标进行对比检测,发现改进的YOLO V3算法能够有效降低小目标漏检率,大大提高检测的平均精度和检测速度。实验结果表明,在KITTI数据集上,改进的YOLO V3算法检测目标的平均精度达到92.43%,与未改进的YOLO V3算法相比提高了2.36%,且检测速度达到44.52?帧/s。

关键词: 目标检测, YOLO V3, 聚类算法, 损失函数