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Article type: Research Article
Authors: Sui, Duob | Gao, Pengb | Fang, Minhangc | Lian, Jinga; b | Li, Linhuia; b; *
Affiliations: [a] State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, Liaoning, China | [b] School of Automotive Engineering, Dalian University of Technology, Dalian, Liaoning, China | [c] Beijing Institute of Space Launch Technology, Beijing, China
Correspondence: [*] Corresponding author. Linhui Li, School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China. E-mail: lilinhui@dlut.edu.cn.
Note: [1] This work was supported in part by the National Natural Science Foundation of China under Grant 61976039, Grant 52172382, and in part by the Science and Technology Innovation Fund of Dalian under Grant 2021JJ12GX015, and in part by the China Fundamental Research Funds for the Central Universities under Grant DUT22JC09.
Abstract: Aiming at the problems of low precision and low real-time performance when deploying to embedded platforms in existing multi-task networks, this paper proposes a traffic scene multi-task perception network model (ETS_YOLOP) based on feature fusion. Firstly, an Efficient Attention Control Aggregation Network Module (EACAN) is constructed to improve the real-time perception of the model, and the Space Pyramid Pool Fast Convolutional Module (SPPFCSPC) is used at the end of the backbone network to increase the receptive field. Finally, a Multiscale Convolution Transformer Fusion Module (CTFM) is designed in the task branch to better capture global information and rich context information. The experimental results show that compared with the YOLOP model, the ETS_YOLOP model has a significant improvement in perception accuracy, 156% in real-time performance, 0.4% increase in mAP on the object detection task, 0.5% increase in mIoU on the drivable area segmentation task, and an 11.4% increase in accuracy on the lane detection task. In order to verify the real-time perception of the model on the embedded platform, the ETS_YOLOP model is deployed on the Huawei MDC300F computing platform. Under the condition of the image input size of 640×640, the average frame rate can reach 55FPS, which can realize real-time perception on the embedded platform.
Keywords: Traffic scene, multi-task perception, self-attention, feature fusion, intelligent vehicle
DOI: 10.3233/JIFS-235246
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5753-5765, 2024
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