融合双注意力机制的人群计数算法

针对背景复杂、遮挡、人群分布不均等人群计数常见问题,提出了一种结合联合损失的空间-通道双注意力机制卷积神经网络模型(joint loss-based space-channel dual attention network, JL-SCDANet).该网络前端进行图像粗粒度特征提取,中间加入空间注意力机制以及通道注意力机制突出图像重点区域,后端使用可加大感受野且不丢失图像分辨率的空洞卷积提取深层二维特征.此外,该模型结合联合损失函数进行训练,以增强模型的鲁棒性.为了验证模型的改进效果,在3个公共数据集(ShanghaiTech Part B、mall和UCF＿CC＿50)上分别进行了对比实验,在ShanghaiTech Part B数据集中平均绝对误差(MAE)和均方误差(MSE)分别达到了8.13和13.13;在mall数据集中MAE、MSE达到了1.78和2.28;在UCF＿CC＿50数据集中MAE、MSE分别达到了182.12和210.24,实验结果证明了该网络在提高人数统计准确率上的有效性.

Crowd Counting Algorithm Based on Dual Attention Mechanism

Given the common problems of crowd counting with a complex background, occlusion, and uneven crowd distribution, a joint loss-based space-channel dual attention network (JL-SCDANet) is proposed. The front end of the network extracts coarse-grained features of an image, and the spatial attention mechanism and channel attention mechanism are added in the middle to highlight the key areas of the image, while the back end uses dilated convolution that can increase the receptive field without losing the image resolution to extract deep two-dimensional features. In addition, the model is trained with the joint loss function to enhance its robustness. Comparative experiments are carried out on three public data sets (i.e., ShanghaiTech Part B, mall, and UCF_CC_50) to verify the improvement effect of the model. In terms of the mean absolute error (MAE) and mean square error (MSE), the results on ShanghaiTech Part B, mall, and UCF_CC_50 reach 8.13 and 13.13, 1.78 and 2.28, and 182.12 and 210.24, respectively. The experimental results prove the effectiveness of the network in improving the accuracy of population statistics.