基于改进YOLOv5的小目标检测

本文针对图像中小目标难以检测的问题, 提出了一种基于YOLOv5的改进模型. 在主干网络中, 加入CBAM注意力模块增强网络特征提取能力; 在颈部网络部分, 使用BiFPN结构替换PANet结构, 强化底层特征利用; 在检测头部分, 增加高分辨率检测头, 改善对于微小目标的检测能力. 本文算法在人脸瑕疵数据集和无人机数据集VisDrone2019两份数据集上均进行了多次对比实验, 结果表明本文算法可以有效地检测小目标.

Small Target Detection Based on Improved YOLOv5

In this study, an improved model based on you only look once version 5 (YOLOv5) is proposed to solve the problem of difficult detection of small targets in images. In the backbone network, a convolutional block attention module (CBAM) is added to enhance the network feature extraction ability. As for the neck network, the bi-directional feature pyramid network (BiFPN) structure is used to replace the path aggregation network (PANet) structure and thereby strengthen the utilization of low-level features. Regarding the detection head, a high-resolution detection head is added to improve the ability of small target detection. A number of comparative experiments are conducted, respectively, on a facial blemish dataset and an unmanned aerial vehicle (UAV) dataset VisDrone2019. The results show that the proposed algorithm can effectively detect small targets.