Robust detection model for multitype sight scoring for infrared zero position motion measurement

Abstract

During the infrared zero movement measurement process, affected by the diversity of divisions of the unknown sight being measured, it is difficult for traditional algorithms to maintain stable identification and positioning accuracy. In order to solve this problem, this paper proposes a robust detection method for multi-type sight divisions based on the YOLOv5 target detection model. First, Transformer Integrates Multiple Convolutional Modules (TIMCM) is added to the C3 module of the YOLOv5 backbone network to enhance the network's feature expression ability for multi-type sight divisions and improve the model's global perception and interaction capabilities; secondly, In the Neck part, a jump bidirectional feature map enhancement structure (BiFPN with Diversified Scale, BDS) is designed to use jump connections to take into account feature information of large, medium and small scales and perform weighted fusion to improve the model's accuracy of sight target information of different scales. Extraction accuracy; finally, a highresolution detector (HRD) is added to the Head part to further improve the detection accuracy of low-resolution targets. Experimental results show that when the improved algorithm identifies and locates conventional infrared sight divisions, the accuracy of false detection or missed detection is reduced, the stability of the algorithm is significantly enhanced, the accuracy reaches 96.5%, the recall rate reaches 97.4%, and the average precision average It reached 97.1%, and it can accurately locate unknown aiming reticles, which shows that the algorithm has strong robustness and generalization, and has certain practical value and research significance in the measurement of zero position movement of infrared sights.