A Novel Equipment Based on Improved YOLOv5s for Automated Electrical Cover Handling

With the rapid development of the power industry, the number of power cables has increased significantly, making the improvement of maintenance efficiency a critical issue. Reducing accidents caused by the manual handling of heavy electrical cover plates has become a pressing concern. However, the maintenance of power cables often requires precise positioning and efficient movement of these heavy cover plates in energy-poor environments. Existing object detection models typically not only require high-performance computing but also have with high cost and large volume, rendering them quite unsuitable for deployment in the resource-constrained system. To address these challenges, we propose an automated solution that integrates negative pressure adsorption and computer vision techniques. The core module of this method is Cover Plates Edge Line-YOLO (CPEL-YOLO), a lightweight model based on You Only Look Once (YOLO). In CPEL-YOLO, it leverages MobileNetV3 with efficient multiscale attention (EMA) and C3-Faster with EMA to focus the model on precise edge lines with reduced computational complexity. Additionally, Scylla-IoU (SIoU) is incorporated to optimize the training process. A dataset of 1009 cover plate images was constructed for training and evaluation. Experimental results show that CPEL-YOLO achieves a [email protected] of 88.1%, comparable to YOLOv5s, while reducing model size by 42% and improving precision by 4.4%.