ACDet: Enhanced self-attention mechanism for pharmaceutical packaging contour detection

This article introduces ACDet(self-attention and concatenation based detector), an object vector detection and recognition method based on convolutional neural networks. This method aims to efficiently detect dense and unordered pharmaceutical packaging contours under varying lighting conditions. By employing combined image enhancement techniques, the method enhances the model's ability to learn the appearance features of objects. The C2F-A (C2F with attention) computational module utilizes multiple gradient flows for multidimensional self-attention enhancement, encompassing both feature and spatial dimensions. The WConcat (weighted concatenation) module facilitates weighted concatenation of various levels of feature maps, capturing more critical features, thereby enhancing the network's cognitive ability. In experiments on the CPPD (cancer pathological and pharmaceutical dataset) for pharmaceutical cases, ACDet achieved 81.0% \textmAP (mean average precision) and 79.5% \textSmooth\textmAP , outperforming other YOLO(you only look once) architecture models by an average of 5.5% to 16.6%, and leading by 0.7% to 6.9% on public datasets. Additionally, zero-shot testing achieved a review success rate of 99.9%. The research results suggest that ACDet can overcome complex detection scenarios, enhance network robustness, and support intelligent industrial production.