IPCRGC-YOLOv7: face mask detection algorithm based on improved partial convolution and recursive gated convolution

Abstract

In complex scenarios, current detection algorithms often face challenges such as misdetection and omission when identifying irregularities in pedestrian mask wearing. This paper introduces an enhanced detection method called IPCRGC-YOLOv7 (Improved Partial Convolution Recursive Gate Convolution-YOLOv7) as a solution. Firstly, we integrate the Partial Convolution structure into the backbone network to effectively reduce the number of model parameters. To address the problem of vanishing training gradients, we utilize the residual connection structure derived from the RepVGG network. Additionally, we introduce an efficient aggregation module, PRE-ELAN (Partially Representative Efficiency-ELAN), to replace the original Efficient Long-Range Attention Network (ELAN) structure. Next, we improve the Cross Stage Partial Network (CSPNet) module by incorporating recursive gated convolution. Introducing a new module called CSPNRGC (Cross Stage Partial Network Recursive Gated Convolution), we replace the ELAN structure in the Neck part. This enhancement allows us to achieve higher order spatial interactions across different network hierarchies. Lastly, in the loss function component, we replace the original cross-entropy loss function with Efficient-IoU to enhance loss calculation accuracy. To address the challenge of balancing the contributions of high-quality and low-quality sample weights in the loss, we propose a new loss function called Wise-EIoU (Wise-Efficient IoU). The experimental results show that the IPCRGC-YOLOv7 algorithm improves accuracy by 4.71%, recall by 5.94%, mean Average Precision (mAP@0.5) by 2.9%, and mAP@.5:.95 by 2.7% when compared to the original YOLOv7 algorithm, which can meet the requirements for mask wearing detection accuracy in practical application scenarios.