School of Mechanical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010, China.
School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou 014010, China.
Comput Intell Neurosci. 2022 Aug 24;2022:5708807. doi: 10.1155/2022/5708807. eCollection 2022.
In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate.
In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN "mid-term" fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection.
MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%.
MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.
在拥挤的人群图像中，传统的检测模型通常存在多尺度目标计数不准确和召回率低的问题。
为了解决上述两个问题，本文提出了一种 MLP-CNN 模型，该模型结合 FPN 特征金字塔，可以融合低分辨率和高分辨率语义信息的特征图，计算量少，能有效解决多尺度人头计数不准确的问题。MLP-CNN“中期”融合模型可以有效融合 RGB 人头图像和 RGB-Mask 图像的特征。借助人头 RGB-Mask 标注和自适应高斯核回归，可以生成增强的密度图，有效解决人头检测召回率低的问题。
将 MLP-CNN 模型应用于上海科技大学和 UCF_ CC_ 50、UCF-QNRF 数据集进行测试。实验结果表明，本文提出的方法的误差有了明显的改善，召回率可以达到 79.91%。
MLP-CNN 模型不仅提高了密度图回归中人群计数的准确性，而且提高了多尺度人群头部目标的检测率。
