Intelligent detection algorithm for hidden hazards in YOLOv8 hole covers based on two-step improvement

Addressing the challenges posed by varying shapes and sizes of maintenance hole covers in complex environments, as well as the high computational complexity and numerous parameters of existing models, this study presents an improved two-step YOLOv8 algorithm for detecting maintenance hole hazards. Firstly, the model structure was optimized by incorporating the Multi-Head Self-Attention Mechanism (MHSA), enhancing its capability to detect maintenance hole covers against complex backgrounds. Secondly, the Deformable Convolution Network version 2 (DCNv2) was applied to accommodate the diverse shapes of maintenance hole covers, further increasing the detection precision. Testing on a custom dataset showed that the improved two-step YOLOv8 model achieved a mAP50 of 0.984 at an intersection over union (IoU) threshold of 0.5, a recall of 0.95, and a precision of 0.964. Compared to the original YOLOv8 model, this model demonstrated significant performance improvements with increases of 0.052 in mAP50, 0.089 in recall, and 0.087 in precision. These enhancements sped up the detection of maintenance hole covers and improved accuracy, providing adequate support for practical applications. Future research will consider integrating this model with embedded devices to promote intelligent and efficient urban monitoring.