常态化疫情防控形势下,通过口罩佩戴检测可以及时提醒人们正确佩戴口罩,从而降低公共场合人员交叉感染的风险。针对口罩佩戴检测任务中被遮挡目标和小目标检测困难的问题,提出一种YOLO-Mask算法。该算法以YOLOv3为基础,在特征提取网络中引入注意力机制,以提升模型对显著特征的表达能力;然后使用特征金字塔和路径聚合策略进行特征融合,使细节特征信息得到增强,实现不同层次特征信息的充分利用;最后优化了损失函数。实验表明:对不同场景下的口罩佩戴目标进行检测,YOLO-Mask算法的平均精度均值达到93.33%,相比于原始YOLOv3算法提高7.62%;与其他主流算法相比,该算法具有更好的检测效果和鲁棒性。

Under normalized epidemic prevention and control, mask-wearing detection can promptly remind people to wear masks correctly, thus reducing the risk of cross-infection of people in public places. Aiming at the difficulty of detecting obscured and small targets in the mask-wearing detection task, a YOLO-Mask algorithm is proposed. The proposed algorithm is based on YOLOv3. It introduces an attention mechanism into the feature extraction network to enhance the model's ability to express salient features. Moreover, it uses feature pyramid and path aggregation strategies for feature fusion to enhance detailed feature information and utilize different levels of feature information. The loss function is optimized. The experimental results show that the average accuracy of the YOLO-Mask algorithm is 93.33% for mask-wearing detection targets in different scenarios, which is 7.62% higher than that of the existing YOLOv3 algorithm. The proposed algorithm has better detection results and robustness compared with other mainstream algorithms.