关键词: 目标检测；卷积神经网络；隧道环境；智能交通系统

Abstract: In order to achieve real-time detection of vehicle object in tunnel environment, an improved YOLOv4 object detection algorithm was proposed. First, the Mobilenetv3 network was used to replace the backbone network of the YOLOv4 algorithm which can reduce the amount of algorithm parameters and improve the detection speed; using a hybrid attention mechanism in the Mobilenetv3 network by combining channel and space dimensions to further improve detection accuracy of the algorithm; a lightweight PANet was proposed which used Ghost convolution operation to replace the ordinary convolution operation so to further reduce the amount of algorithm parameters. In order to verify the effectiveness of the designed model, the urban tunnel vehicle dataset is manually labeled. Experiments show that the object detection model designed in this paper has an average accuracy rate of 92.08% on the tunnel vehicle dataset which is similar to the original YOLOv4 network model. The detection accuracy are similar, but the speed is increased by 45.7%.

Key words: Object detection; Convolutional neural network; Tunnel environment; Intelligent transportation system