Break detection is an important part of the diamond wire production process. To address the problems of low sensitivity and lag feedback of existing contact detection， a non-contact wire break detection method is proposed based on machine vision detection of light spots reflected by diamond lines under strong light. Here， the study addresses the limitations of complex spot detection operation and ease of influence from external illumination of traditional image processing by investigating spot target detection on the embedded platform of diamond line spot detection using deep learning. A variety of Yolo-type models were trained and deployed. The problem of poor real-time detection in embedded devices due to the deep network level and large model volume of the original model was also addressed through a lightweight target spot detection model MCA-Yolox based on Yolox. The MobileNetV3 lightweight feature extraction network was used to replace the backbone feature extraction network of the Yolox model， and the model was lightweighted. Then， the enhanced feature extraction network was lightweight using the deep separable convolution and inverted residual structures. Then， combined with the CA attention mechanism， the detection accuracy of the lightweight model was improved. Finally， the improved model was deployed on the embedded platform. The experimental results show that the size and computation amount of the improved model， MCA-Yolox， are reduced to less than 1/3 those of the Yolox model， and compared with Yolox-Tiny and Yolov4-Tiny of the same scale， it has higher detection accuracy. The mAP of the model increased by more than 1%， and the detection speed can reach 30 frames/s after accelerated optimization. In summary， this paper presents a complete industrial detection scheme based on deep learning to detect diamond wire breaks.