关键词: 目标检测, YOLOv3算法, 细化特征图, 多尺度检测, 损失函数

Abstract: In the target detection scene, there are a large number of targets, small target scales, and high overlap of targets, which make target detection low accuracy and difficult. In order to improve the accuracy of target detection and avoid missed detection and false detection as much as possible, a new improved You Only Look Once version3(YOLOv3) target detection algorithm based on boundary limit point features was proposed. Firstly, a boundary enhancement operator Border was introduced, which adaptively extracted boundary features from the limit points of the boundary to enhance the point features and improve the accuracy of target positioning. Then, the accuracy of target detection was further improved by increasing the target detection scale, refining the feature map, and enhancing the fusion of the feature image deep and shallow semantic information. Finally, based on the target instance characteristics in target detection and the improved network model, the Complete Intersection over Union (CIoU) function was introduced to improve the original YOLOv3 loss function to improve the convergence speed and recall rate of the detection frame. Experimental results show that, the improved YOLOv3 target detection algorithm has a greater detection accuracy compared with the original YOLOv3 target detection algorithm, and the detection speed is similar to the original algorithm, which can effectively improve the target detection ability of models.

Key words: target detection, You Only Look Once version3 (YOLOv3) algorithm, refinement feature map, multi-scale detection, loss function