A bullet recognition algorithm based on improved YOLOX - Hu, Jie; Chen, Hua; Xu, Yubo; Fu, Yu (Frontiers in Physics, 2023)
The identification and positioning of flying projectiles is a critical issue that affects the testing accuracy of equipment in ballistic testing technology. Traditional image processing methods are difficult to accurately extract targets due to the characteristics of small target size, fast speed, and strong fragmentation interference of projectiles ejected from the muzzle. This paper proposes a projectile recognition algorithm based on an improved YOLOX detection model for the detection and recognition of flying projectiles. The fast and accurate YOLOX model is used, and the network structure is improved by adding attention mechanisms in the feature fusion module to enhance the detection ability of small targets; the loss function is also improved to enhance the model's iterative optimization ability. Test results show that the improved YOLOX model has significantly improved accuracy in projectile recognition compared to the original network, reaching 80%, demonstrating the feasibility of the proposed approach.