The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage. This task is time-consuming, prone to human error, halts operation, and may result in the premature disposal of ropes. Therefore, we propose using deep learning and computer vision methods to automate the process of detecting damaged ropes. Specifically, we present a novel vision-based system for detecting damage in synthetic fiber rope images using convolutional neural networks (CNN). We use a camera-based apparatus to photograph the lifting rope's surface, while in operation, and capture the progressive wear-and-tear as well as the more significant degradation in the rope's health state. Experts from Konecranes annotate the collected images in accordance with the rope's condition; normal or damaged. Then, we pre-process the images, design a CNN model in a systematic manner, evaluate its detection and prediction performance, analyze its computational complexity, and compare it with various other models. Experimental results show the proposed model outperforms other techniques with 96.4% accuracy, 95.8% precision, 97.2% recall, 96.5% F1-score, and 99.2% AUC. Besides, they demonstrate the model's real-time operation, low memory footprint, robustness to various environmental and operational conditions, and adequacy for deployment in industrial systems.

翻译：磨损的起重机提升绳索存在的健康与安全危害要求定期进行损伤检测。该任务耗时、易受人为失误影响、会中断作业，并可能导致绳索过早报废。为此，我们提出采用深度学习和计算机视觉方法实现损伤绳索检测的自动化。具体而言，我们提出了一种基于视觉的新型系统，利用卷积神经网络(CNN)检测合成纤维绳索图像中的损伤。我们使用基于相机的装置在运行期间拍摄提升绳索表面，捕获绳索健康状况的渐进磨损以及更显著的老化迹象。科尼公司的专家根据绳索状态（正常或损伤）对采集的图像进行标注。随后，我们对图像进行预处理，系统性地设计CNN模型，评估其检测与预测性能，分析计算复杂度，并将其与多种其他模型进行比较。实验结果表明，所提模型以96.4%的准确率、95.8%的精确率、97.2%的召回率、96.5%的F1分数和99.2%的AUC值优于其他技术。此外，实验还证明了该模型的实时运行能力、低内存占用、对各种环境和运行条件的鲁棒性，以及适用于工业系统部署。