Improved YOLOv8s-Based Detection for Lifting Hooks and Safety Latches

Lifting hooks equipped with safety latches are critical terminal components of lifting machinery. The safety condition of this component is a crucial factor in preventing load dislodgement during lifting operations. To achieve intelligent monitoring of the hook and the safety latch, precise identification of these components is a crucial initial step. In this study, we propose an improved YOLOv8s detection model called YOLO-HOOK. To reduce computational complexity while simultaneously maintaining precision, the model incorporates an Efficient_Light_C2f module, which integrates a Convolutional Gated Linear Unit (CGLU) with Star Blocks. The neck network utilizes Multi-Scale Efficient Cross-Stage Partial (MSEICSP) to improve edge feature extraction capabilities under complex lighting conditions and multi-scale variations. Furthermore, a HOOK_IoU loss function was designed to optimize bounding box regression through auxiliary bounding boxes, and a piecewise linear mapping strategy was used to improve localization precision for challenging targets. The results of ablation studies and comparative analyses indicate that the YOLO-HOOK secured mAP scores of 90.4% at an Intersection over Union (IoU) threshold of 0.5 and 71.6% across the 0.5–0.95 IoU span, thereby eclipsing the YOLOv8s reference model by margins of 4.6% and 5.4%, respectively. Furthermore, it manifested a paramount precision of 97.0% alongside a commendable recall rate of 83.4%. The model parameters were reduced to 9.6 M, the computational complexity was controlled at 31.0 Giga Floating-point Operations Per Second (GFLOPs), and the inference speed reached 310 frames per second (FPS), balancing a lightweight design with excellent performance. These findings offer a technical approach for the intelligent recognition of hooks and safety latches during lifting operations, thus aiding in refining the safety management of lifting operations.