TW-YOLO: High-precision Steel Wire Rope Detection Algorithm Based on Triplet Attention

Addressing the issue of insufficient accuracy in detecting small target defects in steel wire ropes in scenarios such as construction sites and elevators, this paper proposes a steel wire rope defect detection method called TW-YOLO, based on the YOLOv8 network model. Firstly, a Triplet Attention mechanism is introduced after the SPPF module layer of the backbone network to enhance the robustness of the model and its detection accuracy for small defects. Then, the damage function of the original model is replaced with WIOU to further improve the model's detection capability for small targets. In ablation experiments, compared to the original YOLOv8, the TW-YOLO model achieved a 4.45% improvement in detection accuracy for the break category of steel wire ropes and a 3.3% increase in mAP. In comparative experiments, TW-YOLO demonstrated high accuracy and low computational complexity.