Enterprise AI Analysis

Our solution, built on the YOLOv10 architecture, introduces a multi-faceted approach: (1) Advanced Data Enhancement: We address data imbalance and spatial bias by mixing underground coal mine data with diverse open-source ground scene data, augmented with mirroring, rotations, and mosaic techniques. (2) Dynamic Background Sensing Convolution (DBConv): Integrated into the backbone, this module dynamically generates background region masks to filter out complex environmental interference, improving feature extraction for helmet targets. (3) Global-Local Fusion Module (GLFM): Applied in the detection head, GLFM combines high-frequency local features with low-frequency global context to significantly enhance the perception and characterization of small helmet objects. (4) Optimized Loss Function: We refine the CIoU loss function by incorporating an aspect ratio constraint, specifically tailored to the uniform aspect ratio of helmets, accelerating convergence and boosting detection accuracy.

The proposed algorithm demonstrates superior performance across comprehensive evaluations. On a mixed dataset comprising underground and open-source data, it achieved an overall accuracy of 94.4%, recall of 89%, and an average precision (mAP) of 95.4%. Crucially, the system exhibited a 14% reduction in background false detection rates, directly improving reliability in complex environments. Compared to leading mainstream algorithms, our method improved detection accuracy by 6.7% over YOLOv9, 5.1% over YOLOv10, and 11.8% over RT-DETR (for mAP50). Specific to small object detection, the GLFM module alone boosted mAP50 by 8.9% compared to the baseline. These results underscore the algorithm's robustness and efficacy in real-world coal mine scenarios.

This research successfully introduces a novel and practical method for robust helmet detection in challenging underground coal mine environments. By innovatively combining data augmentation strategies, dynamic background perception via DBConv, enhanced small object recognition through GLFM, and a refined loss function, the algorithm significantly outperforms existing state-of-the-art models in accuracy and reliability. This framework provides a powerful tool for real-time worker safety monitoring and standardized operations in mining. While acknowledging the continuous evolution of AI architectures, the core principles of our data processing and structural design remain highly transferable and adaptable to future detection systems, ensuring lasting impact in industrial safety intelligence.