基于轻量化YOLOv3的遥感军事目标检测算法

在导弹智能突防的过程中，从海量的遥感图像数据中检测敌方反导阵地具有极大的应用价值。由于弹载部署环境算力有限，设计了一种兼顾轻量化，检测精确率以及检测速度的遥感目标检测算法。制作了典型遥感军事目标数据集，通过K-means算法对数据集聚类分析。利用MobileNetV2网络代替YOLOv3算法的主干网络，保证网络的轻量化和检测速度。提出了适用于遥感目标特性的轻量化高效通道协同注意力模块和目标旋转不变性检测模块，将其嵌入检测算法中，在网络轻量化的基础上提升检测精确率。实验结果表明，提出算法的精确率达到97.8%，提升了6.8个百分点，召回率达到95.7%，提升了3.9个百分点，平均检测精度达到95.2%，提升了4.4个百分点，检测速度达到了每秒34.19张图，而网络大小仅为17.5?MB。结果表明该算法能满足导弹智能突防的综合要求。

Remote Sensing Military Target Detection Algorithm Based on Lightweight YOLOv3

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.