In weapon range testing，the position of the detonation point provides crucial data support for fuse performance evaluation.To rapidly and accurately identify detonation point regions in images，a detonation point image recognition algorithm is proposed based on YOLOv5-FireCAM network.The method improves the original YOLOv5s network by introducing a lightweight Fire module and using a new C3-Fire network to replace original backbone network.A CAM color attention module is added at the end of the backbone network to enhance detonation point image feature extraction.In the section of the detection head，an Anchor-Free(AF) structure is used to reduce network complexity and computational load while increasing detection accuracy.During model training，the Softmax loss with Hard Negative Mining is adopted as the classification loss function，and the smooth L1 loss function is applied for network bounding box regression in view of the AF structure.Experimental results show that the proposed detection network algorithm achieves an mAP@0.5 of 88.4% on the detonation point dataset，with 28.1% improvement in detection speed and 32.8% reduction in network parameters in comparison with those of original network.This method maintains fast detonation point region identification with high accuracy while reducing network parameters，which is faster and more accurate than the original algorithm.