基于轻量化神经网络的社交距离检测

Social Distance Detection Based on Lightweight Neural Network

保持安全社交距离是有效防止病毒传播的重要手段之一,不仅可以减少感染者数量和医疗负担,同时也极大降低死亡率.在YOLOv4框架基础上使用轻量化网络E-GhostNet代替原网络中的CSPDarknet-53,E-GhostNet网络在输入数据和原始Ghost模块生成的输出特征之间建立关系,使网络能够捕获上下文特征.然后,在E-Ghost Net中引入坐标注意力机制(CA)增强模型对有效特征的关注.另外,使用SIoU损失函数更换CIoU损失获得更快的收敛速度和优化效果.最后,结合DeepSORT多目标跟踪算法来检测和标记行人,并使用仿射变换(IPM)判定行人间距离的违规行为.实验结果显示,该网络检测速度为40 FPS,精度值达到85.71%,相比原始GhostNet算法提升2.57%,达到实时行人距离检测的效果.

Maintaining a safe social distance is one of the important means to effectively prevent the spread of the virus.Moreover,it can not only reduce the number of infected people and ease the medical burden but also greatly lower the mortality rate.On the basis of the you only look once version 4(YOLOv4)framework,the lightweight network EGhostNet is used to replace the CSPDarknet-53 in the original network.The E-GhostNet network establishes a relationship between the input data and the output features generated by the original Ghost module,thereby enabling the network to capture contextual features.Then,the coordinate attention(CA)mechanism is introduced to E-GhostNet to enhance the model’s attention on effective features.In addition,the complete intersection over union(CIoU)loss function is replaced by the soft intersection over union(SIoU)loss function to obtain a faster convergence speed and optimization effect.Finally,the DeepSORT multi-target tracking algorithm is utilized to detect and label pedestrians,and affine transformation(IPM)is employed to determine the violation of the required distance between pedestrians.The experimental results show that the network achieves real-time pedestrian distance detection with a detection speed of 40FPS and an accuracy of 85.71%,which is 2.57%higher than that of the original GhostNet algorithm.