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      A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4

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          Abstract

          Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

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          Deep Residual Learning for Image Recognition

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            Microsoft COCO: Common Objects in Context

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              Squeeze-and-Excitation Networks

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2021
                10 December 2021
                : 2021
                : 9218137
                Affiliations
                1Changchun University of Science and Technology, School of Compute Science and Technology, Changchun, Jilin 130022, China
                2Chengdu University of Technology, Department of Geophysics, Chengdu, Sichuan 610059, China
                Author notes

                Academic Editor: Jianli Liu

                Author information
                https://orcid.org/0000-0001-9349-5724
                Article
                10.1155/2021/9218137
                8683201
                e0e7f69a-1853-47cc-a429-dfd3d934c8e9
                Copyright © 2021 Rui Wang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 October 2021
                : 25 November 2021
                : 26 November 2021
                Funding
                Funded by: Natural Science Foundation of Jilin Province
                Award ID: 20200201053JC
                Categories
                Research Article

                Neurosciences
                Neurosciences

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