Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks

• URL: http://arxiv.org/abs/2302.11947v2
• Date: Thu, 19 Dec 2024 15:13:46 GMT
• Title: Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks
• Authors: Tuomas Jalonen, Mohammad Al-Sa'd, Roope Mellanen, Serkan Kiranyaz, Moncef Gabbouj,
• Abstract summary: Vision-based system for detecting damage in synthetic fiber rope images using lightweight convolutional neural networks.<n>Experts from Konecranes annotate the collected images in accordance with the rope's condition; normal or damaged.<n>Model outperforms other similar techniques with 96.5% accuracy, 94.8% precision, 98.3% recall, 96.5% F1-score, and 99.3% AUC.
• Score: 14.553374494874374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 efficient deep learning and computer vision methods to automate the process of detecting damaged ropes. Specifically, we present a vision-based system for detecting damage in synthetic fiber rope images using lightweight convolutional neural networks. We develop 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, systematically design a deep learning model, 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 similar techniques with 96.5% accuracy, 94.8% precision, 98.3% recall, 96.5% F1-score, and 99.3% 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 applications such as lifting, mooring, towing, climbing, and sailing.

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