Abstract
Subway button detection is paramount for passenger safety, yet the occurrence of inadvertent touches poses operational threats. Camera-based detection is indispensable for identifying touch occurrences, ascertaining person identity, and implementing scientific measures. Existing methods suffer from inaccuracies due to the small size of buttons, complex environments, and challenges such as occlusion. We present YOLOv8-DETR-P2-DCNv2-Dynamic-NWD-DA, which enhances occlusion awareness, reduces redundant annotations, and improves contextual feature extraction. The model integrates the RTDETRDecoder, P2 small target detection layer, DCNv2-Dynamic algorithm, and the NWD loss function for multiscale feature extraction. Dataset augmentation and the GAN algorithm refine the model, aligning feature distributions and enhancing precision by 6.5%, 5%, and 5.8% in precision, recall, and mAP50, respectively. These advancements denote significant improvements in key performance indicators.
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