Aiming at the problems of low accuracy and long inference time in bullet hole detection technology for chest ring targets in shooting sports, a lightweight algorithm based on YOLOv8 is proposed. Firstly, a MobileNetV4 backbone enhanced with a convolutional block attention module (CBAM) is designed to enhance the model′s accuracy while minimizing its computational complexity and the number of parameters. Secondly, a C2fGhost-AFPN-P2 (asymptotic feature pyramid network) feature aggregation network is proposed, which increases the network′s perception of small targets by combining the C2fGhost module, AFPN and P2 output layer. Finally, the loss function of the algorithm is set to WIoU to weaken the impact of the imbalance in the number of positive and negative samples and improve the regression accuracy of the prediction box. The experimental results on the self-made dataset show that the mAP50 is 0.973, which is 16.0% higher than the original algorithm; the number of parameters is 2.43×106, which is 19.3% lower than the original algorithm; and the GFLOP is 8.0, which is 2.4% lower than the original algorithm. The experimental results prove that M-A-YOLO has better performance in bullet hole detection.