基于改进YOLOv8s算法的胸环靶弹孔检测技术OA北大核心CSTPCD

Traditional chest bitmap bullet hole detection technology was easily affected by light intensity and com-plex background in natural conditions.In order to solve the proplem an improved algorithm based on YOLOv8s was designed in this study.Firstly,in order to avoid the impact of complex environment on the accuracy of bullet hole recognition,graph segmentation was introduced in the process of data set production to separate the background from the chest bitmap.Secondly,in order to improve the detection ability of the model to the bullet hole,CBAM attention mechanism was introduced into C2f,and the recognition ability of the network to the target bullet hole was improved by giving different weights to the spatial and channel characteristics.In order to reduce the information loss of bullet hole characteristics in the down sampling process and reduce the probability of missing bullet hole detection,the detection scale was increased to 160×160 small target output layer.Considering that the original con-volutional layer was not sensitive to small targets,the SPD-Conv module was used to replace the original convolu-tional layer to extract more feature information to improve detection accuracy.Finally,the loss function of the boun-ding box was changed to WIoU to weaken the influence of the unbalanced number of positive and negative samples and improve the regression accuracy of the prediction box.The experimental results on the self-made chest bitmap data set showed that the accuracy rate P of the improved algorithm was 96.9%,the recall rate R was 96.4%,and the 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 showed that the improved YOLOv8s model had better performance in the detection of complex environment and dense bullet holes.