基于改进YOLOv3算法的行人检测研究

YOLOv3算法在单一物体目标检测时使用Darknet53作为主干,网络出现冗余现象,导致参数过多,检测速度变慢,传统的边界框损失函数影响检测定位准确性。针对这一问题,文中提出了改进YOLOv3算法的行人检测方法。通过构造以Darknet19为主干网络多尺度融合的新型网络,加快训练速度和检测速度,还通过引入广义交并比损失函数来提高检测精确度。实验结果表明,在行人检测数据集如INRIA行人数据集中,相比于原始算法,文中所提算法的精确度提高了5%。和Faster R-CNN相比,在保证准确率的情况下,采用文中算法使单张图片的检测速度达到了每张0.015 s。

Pedestrian Detection Based on Improved YOLOv3 Algorithm

The YOLOv3 algorithm uses Darknet53 as the backbone in the target detection (pedestrian detection) of a single object, and the network appears redundant, which results in too many parameters and slow detection speed. Additionally, the traditional bounding box loss function makes the detection and positioning inaccurate. To solve these problems, the improved YOLOv3 backbone network is proposed in the current study. A new multi-scale fusion network based on Darknet19 is constructed to accelerate the training speed and detection speed, and a generalized intersection over union loss function is introduced to improve the detection accuracy. The experimental results show that the proposed algorithm improves the accuracy of the original algorithm by 5% in the pedestrian detection dataset such as the INRIA pedestrian dataset. Compared with Faster R-CNN , the detection speed of a single image reaches 0.015 s per image under the condition of good accuracy.