【摘要】 针对遮挡行人检测识别困难、检测精度低，以及漏检率高等问题，在YOLOv7方法的基础上进行结构优化，提出了一种基于多层特征融合的行人检测网络模型，旨在提高遮挡行人检测的准确性。该方法是在主干网络特征提取部分采用ELANC模块，以增强行人特征信息的提取能力，从而提高行人检测的准确性。同时，在多尺度特征融合部分引入全局注意力机制构成多层特征融合，通过跨维度的信息交互，特别是对位置信息的关注，增强检测目标特征的表征，提高行人检测的准确性。此外，为了加速模型的收敛速度，采用EIoU作为损失函数，进一步提升检测框的定位精度。在公开数据集CityPresons上进行训练验证，模型对数平均漏检率MR-2下降，Bare,Partial,Reasonable,Heavy分别下降0.55%,0.91%,1.78%,1.68%，有效减少了漏检率。更多还原

【Abstract】 In view of the difficulty,low detection accuracy and high missed detection rate of occluded pedestrian detection,apedestrian detection network model based on multi-layer feature fusion is proposed based on structural optimization of YOLOv7 method,aiming at improving the accuracy of occluded pedestrian detection.The method is to use ELAN-C module in the feature extraction part of the backbone network to enhance the ability of extracting pedestrian feature information,so as to improve the accuracy of pedestrian detection.At the same time,the global attention mechanism is introduced into the multi-scale feature fusion part to form multi-layer feature fusion.Through inter-dimensional information interaction,especially the focus on location information,the representation of detection target features is enhanced and the accuracy of pedestrian detection is improved.In addition,in order to accelerate the convergence rate of the model,EIoU is used as a loss function to further improve the positioning accuracy of the detection frame.The model is trained and verified on the open data set CityPresons,and the log-average miss rate MR-2 of the evaluation index is decreased Bare 0.55%,Partial 0.91%,Reasonable 1.78%,Heavy 1.68%,respectively,which effectively reduce the miss rate.更多还原