An Innovative Approach to Enhance the Safety of Elevator Using Steel Cable Damage Detection Model Based on YOLO
Keywords:
Deep learning; Convolutional Neural Network; YOLOv5; Steel cables; Attention mechanism; Surface damage detection; Object detection.Abstract
To address the issues such as limited detection device resources and prolonged detection times in surface damage detection of steel cables installed commercial, public, and industrial buildings, advanced deep learning techniques, and Convolutional Neural Networks (CNN) have been investigated in this study and a new network model has been designed. This work proposes a steel cable defect detection network model based on YOLO, incorporating GhostNet into the backbone network, and introducing a novel feature extraction module (ShuffleNC3) based on ShuffleNet and attention mechanisms. Pruning improvements are then applied to the Head part. Experimental results indicate that the improved network achieves approximately1.149% increase in average precision compared to the baseline YOLOv5s. This modification achieves a simultaneous reduction of network computational costs and maintains high recognition accuracy, meeting better requirements for surface damage detection in steel cables. The parameters and computational costs are reduced by approximately 43 % and 31.4%, respectively, while the model size also decreases by 42%.
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