ArXiv Preprint
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.
Tuomas Jalonen, Mohammad Al-Sa’d, Roope Mellanen, Serkan Kiranyaz, Moncef Gabbouj
2023-02-23
