A Real-Time Obstacle Detection Framework for Gantry Cranes Using Attention-Augmented YOLOv5s and EIoU Optimization

To meet the need for efficient and precise detection of people and obstacles in the actual operating environment of a gantry crane, a detection model based on an improved YOLOv5s was proposed which incorporates the parameter-free SimAM attention mechanism to enhance obstacle feature extraction capabilities, employs the EIoU loss function to optimize bounding box regression accuracy, and utilizes preprocessing techniques to improve input image quality. Training experiments on humans and simple simulated obstacles demonstrate that the improved model achieves significantly higher recognition accuracy and speed compared to the original YOLOv5 model. The improved model was applied to the recognition experiments of reducer obstacles under varying sizes, visibility levels, and distance conditions, and the comparative experiments were conducted with mainstream YOLO models, as well as different attention mechanisms and loss functions. The results show that the mAP@0.5 of the improved model achieves 0.884 with superior recognition performance and used lower computational resource requirements, providing a reliable solution for real-time obstacle detection in crane operation scenarios.