An Improved D-FINE Method for Concurrent Detection of Multiple Hazardous Behaviors in Elevator Maintenance Environments

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To address the challenges of indistinct local details, difficult target boundary localization, and the concurrent occurrence of multiple hazardous behaviors in elevator shaft and car-top maintenance environments, this study proposes a high-resolution detail-enhanced hazardous behavior detection method based on D-FINE-S. First, we design a P2V2 shallow detail enhancement module that injects high-resolution P2 features into P3 after channel projection and spatial alignment, thereby supplementing local detail information without introducing an additional P2 detection head. Second, we propose a Residual-Adaptive Fine-grained Distribution Refinement (RA-FDR) mechanism, which applies bounded residual calibration to the non-uniform projection positions of the original FDR to improve the adaptability of bounding box regression to the data distribution of the target task. Overall, the final model achieves mAP@0.5 and mAP@0.5:0.95 values of 92.1% and 54.7%, respectively, representing improvements of 4.1 and 3.6 percentage points over D-FINE-S. The results indicate that the proposed method improves hazardous behavior detection and localization performance on the current dataset of elevator maintenance images.