Lightweight crowd counting method based on regional information aggregation
Aiming at the problems of loss of detailed information in high-density crowd images,background noise being easily confused with crowd features,and high complexity of network models,this article proposes a lightweight crowd counting method based on regional information aggregation. First,a multi-scale feature extraction module based on channel activation is designed to obtain fine-grained multi-scale features in high-density images. This module introduces Ghost convolution to construct an inter-layer hierarchical residual connection structure,and sup-plementing each level of features with channel activation,achieving a gradual expansion of the network' s receptive field in a lightweight manner. Secondly,a self-attention region information aggregation module is proposed to obtain feature information from regions of different scales. This module integrates region information from both channel and spatial dimensions using a lightweight self-attention mechanism,enhancing focus on crowd features to weaken the impact of background noise on counting. Finally,considering the instability in the convergence process of the origi-nal count loss,a new counting loss is introduced based on the DM-Count loss,which improves the model stability and counting sensitivity,and further improves the counting performance. Experimental results on four public data sets of Shanghai Tech,UCF-QNRF,JHU-CROWD++,and NWPU-Crowd show that the method proposed in this thesis has a specific improvement compared with other mainstream lightweight crowd counting methods,and the number of model parameters remains relatively low-level.
crowd countingregional information aggregationlightweightself-attentionloss function
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