A Crowd QR Code Detection Method Combining Multi-perspective Images with PP-YOLOE
The existing target detection system is still unable to effectively achieve batch automated detection of extremely small QR(Quick Response)codes in crowded scenarios.To this end,a crowd QR code assisted detection method based on multi per-spective images and improved PP-YOLOE model is proposed.Firstly,a multi-perspective image acquisition system is con-structed to accurately associate multiple target subjects using side and top-view images.Subsequently,a cross-layer spatial atten-tion module is added to the Path Aggregation Network(PAN)to enhance the model's ability to detect small targets.Secondly,the RepResBlock module is lightweight improved using deep separable convolution,which improves the efficiency of the model algorithm execution.In the final comparative experiment,the proposed algorithm outperforms the other four algorithms,achiev-ing a 9.9%improvement in effective target detection accuracy.It achieves 13 detections in a single attempt,with an average time of 72.5ms for single target detection.