改进的YOLOv3算法及其在军事目标检测中的应用

复杂环境下军事目标检测技术是提高战场态势生成、分析能力的基础和关键.针对军事目标检测任务在复杂环境下传统检测算法的检测性能较低问题,提出一种基于改进YOLOv3的军事目标检测算法,通过深度学习实现复杂环境下军事目标的自动检测.构建军事目标图像数据集,为各类目标检测算法提供测试环境;在网络结构上通过引入可形变卷积改进的R...

Improved YOLOv3 Algorithm and Its Application in Military Target Detection

Military target detection in a complex environment is the basis and key to improving battlefield situation generation and analysis capability. For the military target detection tasks, the detection performance of traditional detection algorithms in complex environment is low. A military target detection algorithm based on improved YOLOv3 algorithm is proposed to automatically detect the military targets in complex environment through deep learning. A military target image dataset is constructed to provide a testing environment for various target detection algorithms. The detection accuracy and speed of deformable target are improved by introducing the deformable convolutional improved ResNet50-D residual network as feature extraction network. In the stage of feature fusion, a dual-attention mechanism and feature reconstruction module are introduced to enhance the characterization ability of target features, suppress the interference, and improve the detection accuracy. The loss function of target detector is redesigned by using DIOU Loss functions and Focal Loss to funther improve the detection accuracy of military targets. The experimental results show that the improved YOLOv3 algorithm improves the average detection accuracy by 2.98% and the detection speed by 8.6 frames/s compared with the original YOLOv3 algorithm. The improved YOLOv3 algorithm has better detection performance and can provide effective auxiliary technical support for battlefield situation generation and analysis.