关键词: YOLOv3算法, 目标检测, GIou loss, Focal loss

Abstract:

As an object detection algorithm, YOLOv3 can achieve fast detection speed and high detection accuracy. However, YOLOv3 has the problems of low detection accuracy for small objects and inaccurate boundary box location. In this paper, an improved YOLOv3 object detection algorithm is proposed, which increases the bypass connection between residual blocks in darknet-53 and further reuses the features to extract more feature information. At the same time, GIoU loss is used as the loss of bounding box, which can make the network optimize in the direction of high overlap between prediction box and ground truth. In addition, in order to reduce the error caused by the imbalance of positive and negative samples, Focal loss is added to the loss function. The experimental results on PASCAL VOC and COCO datasets show that the improved YOLOv3 algorithm can improve the accuracy of object detection on the premise of real-time performance. Compared with YOLOv3, the improved YOLOv3 algorithm reaches 83.7 mAP（IoU=0.5） in PASCAL VOC 2007 test and improves 2.27mAP（IoU [0.5, 0.95]）on the COCO datasets.

Key words: YOLOv3 algorithm, object detection, GIoU loss, Focal loss