DSCA-Net: Indoor Head Detection Network Using Dual-Stream Information and Channel Attention

We propose a novel indoor head detection network using dual-stream information and multi-attention that can be used for indoor crowd counting. To solve the problem of object scale diversity in indoor human head detection, especially the problem of smallscale human head, we propose a dual-stream information flow structure to enrich the positioning and category semantic information of small-scale objects. We propose a kind of structure of the channel-attention mechanism which is used to enhance the ability of the network to identify small-scale objects. Our method has achieved a recall rate of 0.91 and an F1 score of 0.92 on SCUT-HEAD, which achieves the state-of-art performance in the field of indoor crowd detection.