TWRD-Net:一种用于曳引钢丝绳缺陷的实时检测网络算法

TWRD-Net:A real-time detection network algorithm for traction wire rope defects

曳引钢丝绳(TWR)在大型工业起重设备中发挥着重要的应用价值。在使用曳引钢丝绳作业的同时,也需要定期对曳引钢丝绳进行缺陷诊断避免安全事故发生。传统方法是人工目测方法,但该种方法检测时间长、效率低下。因此,本文提出了一种基于YOLOv5改进网络的曳引钢丝绳缺陷(TWRD)检测的网络算法,简称TWRD-Net。为了方便在算力较低的工业设备上进行部署,首先设计了轻量级的LW-C3模块,降低了模型的参数量以及计算开销;其次,改进PAN结构,设计了跨层级加权特征金字塔网络(CLW-FPN)结构,加强模型对于缺陷语义信息的提取、对缺陷位置信息的敏感度;最后,本文设计了β-CIoU损失函数,相较于CIoU损失函数,β-CIoU降低了边界框回归损失,并进一步提升了检测精度。本文建立了TWRD数据集,并使用TWRD-Net进行实验,实验结果表明,本文提出的TWRD-Net准确率可达98%,mAP可达99.4%,帧率可达151 fps,对比其他主流检测模型实验结果,具有精度高、体积小和检测速度快的优势,可为工业设备质检人员提供参考依据。

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 designsβ-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.