• 图形图像处理 •

### 改进YOLOv4的密集遥感目标检测

1. 成都信息工程大学 软件工程学院，成都 610225
• 出版日期:2021-11-15 发布日期:2021-11-16

### Improved YOLOv4 for Dense Remote Sensing Target Detection

XIE Junzhang, PENG Hui, TANG Jianfeng, HOU Yichen, ZENG Qingxi

1. College of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
• Online:2021-11-15 Published:2021-11-16

Abstract:

Based on the problems of missed detection and poor detection effect of remote sensing targets in densely distributed and complex background scenes due to insufficient feature extraction and expression capabilities, an improved YOLOv4 remote sensing target detection algorithm is proposed. The anchor uses the [K]-means clustering algorithm to re-cluster for reducing the amount of network calculation; improve the feature extraction network structure, introduce residual connections to eliminate continuous convolution operations in the network to improve the feature extraction capabilities of dense targets; in the feature extraction network, the activation function adds an adaptive feature activation smoothing factor, and the Mish activation function is used in the PANet feature fusion network structure to enhance the network’s ability of extracting nonlinear features, enhance the network’s feature extraction ability, and improve the denseness of remote sensing targets and the detection effect. The proposed algorithm and the original YOLOv4 target detection algorithm are compared on the remote sensing image data set. The improved YOLOv4 algorithm has an average accuracy rate（mAP） of 85.05% on the remote sensing image data set selected in the experiment, which is similar to the YOLOv4 algorithm. Compared with that, mAP is increased by 5.77 percentage points. The experimental results show that the improved YOLOv4 algorithm has better performance under complex background conditions such as single target dense distribution and multi-target mixed distribution.