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

• URL: http://arxiv.org/abs/2302.11947v1
• Date: Thu, 23 Feb 2023 11:44:43 GMT
• Title: Real-Time Damage Detection in Fiber Lifting Ropes Using Convolutional Neural Networks
• Authors: Tuomas Jalonen, Mohammad Al-Sa'd, Roope Mellanen, Serkan Kiranyaz, and Moncef Gabbouj
• Abstract summary: We present a vision-based system for detecting damage in synthetic fiber rope images using convolutional neural networks (CNN) Experts from Konecranes annotate the collected images in accordance with the rope's condition; normal or damaged. 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.
• Score: 19.093832934990044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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