Traction wire rope (TWR) plays an important application value in large-scale industrial lifting equipment. While using the traction wire rope for operation, it is necessary to regularly diagnose the defects of the traction wire rope to avoid safety accidents. The traditional method is manual visual inspection, but this method has long detection time and low efficiency. Therefore, this article proposes a network algorithm for detecting traction wire rope defect ( TWRD) based on YOLOv5 improved network, abbreviated as TWRD-Net. In order to facilitate deployment on industrial equipment with low computational power, a lightweight LW-C3 module is first designed to reduce the model′s parameter count and computational overhead. Secondly, the PAN structure is improved by designing a CLW-FPN structure to enhance the model′s sensitivity to defect semantic information extraction and defect location information. Finally, this article designs β-CIoU loss function. Compared with CIoU Loss function, β-CIoU reduces the loss of bounding box regression and further improves detection accuracy. This article establishes a TWRD dataset and conducts experiments by using TWRD-Net. The experimental results show that the accuracy of the proposed TWRD-Net can reach 98% , mAP can reach 99. 4% , and frame rate can reach 151 fps. Compared with other mainstream detection model experimental results, it has the advantages of high accuracy, small size, and fast detection speed, which can provide reference for industrial equipment quality inspectors.