During the detection of wire breakage defects in the constant-diameter growth process of monocrystalline silicon production，an improved YOLOv8-RCA network framework was proposed based on the YOLOv8 object detection model. First，the RFAConv convolution was introduced to enhance the model’s ability to capture features at different scales by incorporating receptive field attention. Second，a novel C2f_Atten module was designed with the addition of CI_Attention，which enabled the model to focus more effectively on key regions during detection and aimed to improve accuracy. Furthermore，the AFGCAttention mechanism was introduced to enhance the diversity of features input into the attention head and reduce the computational redundancy of multi-head attention. Experimental results show that YOLOv8-RCA achieved excellent performance in multiple aspects. The Precision，Recall，mAP₅₀，and mAP_50-95 increase by 12.6%，6.2%，8.1%，and 5.6%，respectively，compared with the baseline model. The model was tested and verified using a constant-diameter growth process wire breakage dataset and is applicable to actual monocrystalline silicon production.