A study on the detection of conductor quantity in cable cores based on YOLO-cable.

The quantity of cable conductors is a crucial parameter in cable manufacturing, and accurately detecting the number of conductors can effectively promote the digital transformation of the cable manufacturing industry. Challenges such as high density, adhesion, and knife mark interference in cable conductor images make intelligent detection of conductor quantity particularly difficult. To address these challenges, this study proposes the YOLO-cable model, which is an improvement made upon the YOLOv10 model. Specifically, the Focal loss function is introduced, the C2F structure in the backbone is optimized, the Focal NeXt module is added, and a multi-scale feature (MSF) module is incorporated in the Neck section. Comparative experiments with various YOLO series models demonstrate that the YOLO-cable model significantly outperformed the baseline YOLOv10s model as it achieves recall, mAP0.5, and mAP scores of 0.982, 0.994, and 0.952, respectively. Further visualization analysis shows that the overlap of YOLO-cable detection boxes with manually labeled samples reaches 90.9% in length and 95.7% in height, indicating high data consistency. The IOU threshold adopted by the model enables it to effectively filter out false detection, thus ensuring detection accuracy. In short, the proposed model excels in detecting the number of cable conductors, enhancing quality control in cable production. This study provides new insights and technical support for the application of deep learning in industrial inspections.