基于改进RTMDet算法的自动扶梯行人异常行为检测OA

For abnormal behavior detection tasks in escalator scenes,such as falling,squatting,and car-rying luggage,existing methods still face problems including severe background interference,difficulties in detecting small objects,heavy target occlusion,and imbalanced category distribution.Existing methods struggle to balance detection accuracy and real-time performance.To address these issues,an improved abnormal behavior detection model for escalators is proposed based on RTMDet framework.Specifically,the CPCA attention mechanism is incorporated into the backbone network to enhance the model's ability to extract features from critical regions and informative cues.An Adaptive Spatial Feature Fusion Module(ASFM)is embedded into the neck network to improve multi-scale feature fusion and strengthen the rep-resentation of small and occluded targets.In addition,the loss function is refined to alleviate the problem of category imbalance and improve bounding box regression accuracy.Experimental results show that the pro-posed method achieves good detection performance on a self-built escalator abnormal behavior dataset,with a Pre-cision of 91.8%,Recall of 91.2%,mAP of 94.9%,and FPS of 48.1.Compared with the baseline RTMDet,the im-proved model increases mAP by 4.8 percentage points,indicating that the proposed method can effectively improve the accuracy and robustness of escalator abnormal behavior detection while maintaining real-time performance.