Ghostcount: A Lightweight Convolution Network Based on High-Altitude Video for Vehicle Instantaneous Counting in Dense Traffic Scenes

Instantaneous vehicle counting of traffic scenes based on high-altitude video is an important way for real-time traffic information collection in intelligent transportation systems (ITS). However, vehicle counts based on high-altitude video are susceptible to problems such as denseness, occlusion and small size. The mainstream method is to use a Convolutional Neural Network (CNN) to output density maps and obtain vehicle count results. However, most CNNs are computationally expensive and have poor real-time performance. Therefore, the authors propose a lightweight CNN named GhostCount, specially designed for high-accuracy vehicle counts on edge devices. First, the authors combine ResNet-18 and Lightweight RefineNet to build an encoder–decoder network architecture to effectively extract vehicle features in complex traffic scenes. Next, the authors replace the ordinary convolutional layers in ResNet-18 with Ghost modules to lighten the network. Finally, a binary cross-entropy loss function is introduced to suppress background noise. The authors demonstrate GhostCount on public datasets (TRANCOS, CARPK, PUCPR+) and the authors' self-built dataset (CSCAR). Results show that GhostCount can perform instantaneous vehicle counting with higher accuracy and faster inference speed than other representative lightweight CNNs. The method the authors propose would provide new solutions and ideas for ITS applications such as traffic information collection and smart parking management.


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  • Accession Number: 01882978
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 23 2023 10:09AM