关键词: 炮弹定位, 显著性目标检测, 注意力机制, 金字塔池化, 深度可分离卷积

Abstract: Aiming at the problems of hidden danger, low efficiency of manual measurement and poor accuracy of current projectile flame location methods, an improved projectile flame detection algorithm based on salient target detection network BASNet (Boundary-Aware Salient Object Detection) is proposed in this paper. Using the improved BASNet network, combined with attention mechanism module-CBAM (Convolutional Block Attention Module), pyramid pooling module-PPM (Pyramid Pooling Module) and depth separable convolution, to detect the projectile fire and extract the coordinates of impact point on the image. The experimental results show that detection accuracy of F measure reaches 0.914, the mean absolute error-MAE reaches 0.006 and detection speed reaches 3.86 fps,better than other salient object detection network like BASNet, U2Net. The error between the coordinates of impact point image extracted by this method and the real coordinates is 5.92 pixels, which is 4.85 pixels less than BASNet. In conclusion, the improved network retains the effective shallow semantic information, enhances the detection accuracy of the network for the significant objects, improves the efficiency of model reasoning, and is suitable for the detection of small-scale projectile fire smoke in the shooting range, which can meet the actual needs of the application in the shooting range.

Key words: projectile positioning, salient object detection, attention mechanism, pyramid pooling, depth separable convolution