Infrared Sight Zero Detection Based on Camera Pose Correction Model

Yanfeng, 李延风 Li; Zhiyong, 安志勇 An; Danni, 李丹妮 Li; Jinsong, 王劲松 Wang

doi:10.3788/cjl201441.0908003

Cited by 1 publication

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“…In the measurement of zero displacement, traditional image processing techniques are often used for the identification and positioning of sight lines. Reference [1][2][3] designed an infrared sight zero position detection method based on a camera pose correction model, using traditional methods such as the center of gravity method and Zernike moment invariance. However, this algorithm is only applicable to several common cross or arrow segmentation types.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Zhang

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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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.

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Section: Introductionmentioning

confidence: 99%