Fast intensive crowd counting model of Internet of Things based on multi‐scale attention mechanism

Abstract

AbstractObject detection based on deep learning plays an important role in the application of the Internet of Things (IoT). Traditional methods consume a lot of computing resources and cannot be well deployed in the IoT environment. A lightweight object detection method based on attention mechanism is proposed and applied to crowd counting. In view of the low accuracy and poor real‐time performance of multi‐scale crowd detection, we design a crowd counting model based on YOLO v5, and apply it to the IoT environment. It is proposed to insert the transformer into the YOLO v5 backbone network. Based on the multi‐head attention mechanism in the transformer encoder, the global dependency is modelled to make full use of the context information. The CNN is used to realize the fusion of multi‐scale feature maps, and the feature enhancement modules concerned by the attention network are further counted. Experiments show that it can not only detect multi‐scale targets, but also achieve real‐time performance in video surveillance scenes.