Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 263-269.

### Remote Sensing Military Target Detection Algorithm Based on Lightweight YOLOv3

QIN Weiwei, SONG Tainian, LIU Jieyu, WANG Hongwei, LIANG Zhuo

1. 1.School of Nuclear Engineering, Rocket Force University of Engineering, Xi’an 710025, China
2.School of Artificial Intelligence, OPtics and ElectroNics（iOPEN）, Northwestern Polytechnical University, Xi’an 710072, China
3.China Academy of Launch Vehicle Technology, Beijing 100076, China
• Online:2021-11-01 Published:2021-11-04

### 基于轻量化YOLOv3的遥感军事目标检测算法

1. 1.火箭军工程大学 核工程学院，西安 710025
2.西北工业大学 光电与智能研究院，西安 710072
3.中国运载火箭研究院，北京 100076

Abstract:

In the process of intelligent missile penetration, detecting enemy anti-missile positions from massive remote sensing image data has great application value. Due to the limited computing power of the missile-borne deployment environment, this paper designs a remote sensing target detection algorithm that takes into account lightweight, detection accuracy and detection speed. A typical remote sensing military target data set is produced, and the data set is clustered and analyzed by the K-means algorithm. The MobileNetV2 network is used to replace the backbone network of the YOLOv3 algorithm to ensure the lightweight and detection speed of the network. A lightweight and efficient channel coordinated attention module and a target rotation invariance detection module suitable for remote sensing target characteristics are proposed, and they are embedded in the detection algorithm to improve the detection accuracy on the basis of network lightweight. Experimental results show that the accuracy rate of the algorithm in this paper reaches 97.8%, an increase of 6.7 percentage points, the recall rate reaches 95.7%, an increase of 3.9 percentage points, the average detection accuracy reaches 95.2%, an increase of 4.4 percentage points, and the detection speed reached 34.19 images per, and the network size is only 17.5?MB. The results show that the algorithm in this paper can meet the comprehensive requirements of intelligent missile penetration.