Bullet Hole Detection Technology of Chest Bitmap Based on Improved YOLOv8s Algorithm

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Bullet Hole Detection Technology of Chest Bitmap Based on Improved YOLOv8s Algorithm

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[1]SU Yufeng,BIAN Feng,ZHANG Yutang.Bullet Hole Detection Technology of Chest Bitmap Based on Improved YOLOv8s Algorithm[J].Journal of Zhengzhou University (Engineering Science),2024,45(05):16-22.[doi:10.13705/j.issn.1671-6833.2024.05.013]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 05 Page number: 16-22 Column: Public date: 2024-08-08

Title:: Bullet Hole Detection Technology of Chest Bitmap Based on Improved YOLOv8s Algorithm

Author(s):: SU Yufeng¹; BIAN Feng¹; ZHANG Yutang²; 1. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Zhengzhou Weida AutomationTechnology Co. , Ltd. , Zhengzhou 450052, China

Keywords:: YOLOv8s; bullet hole detection; CBAM attention mechanism; loss function; SPD-Conv

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2024.05.013

Abstract:: Traditional chest bitmap bullet hole detection technology was easily affected by light intensity and complex background in natural conditions. In order to solve the proplem an improved algorithm based on YOLOv8s wasdesigned in this study. Firstly, in order to avoid the impact of complex environment on the accuracy of bullet holerecognition, graph segmentation was introduced in the process of data set production to separate the backgroundfrom the chest bitmap. Secondly, in order to improve the detection ability of the model to the bullet hole, CBAMattention mechanism was introduced into C2f, and the recognition ability of the network to the target bullet hole wasimproved by giving different weights to the spatial and channel characteristics. In order to reduce the informationloss of bullet hole characteristics in the down sampling process and reduce the probability of missing bullet holedetection, the detection scale was increased to 160×160 small target output layer. Considering that the original convolutional layer was not sensitive to small targets, the SPD-Conv module was used to replace the original convolutional layer to extract more feature information to improve detection accuracy. Finally, the loss function of the bounding box was changed to WIoU to weaken the influence of the unbalanced number of positive and negative samplesand improve the regression accuracy of the prediction box. The experimental results on the self-made chest bitmapdata set showed that the accuracy rate P of the improved algorithm was 96. 9%, the recall rate R was 96. 4%, andthe average accuracy mAP50 was 98. 0%, which were improved by 8. 8 percentage points, 25. 4 percentage points,and 15. 3 percentage points respectively, compared with the original algorithm. The experimental results showedthat the improved YOLOv8s model had better performance in the detection of complex environment and dense bulletholes.

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Last Update: 2024-09-02