CenterNet-Auto: A Multi-object Visual Detection Algorithm for Autonomous Driving Scenes Based on Improved CenterNet

• Published in: IEEE transactions on emerging topics in computational intelligence Vol. 7; no. 3; pp. 1 - 11
• Main Authors: Wang, Hai, Xu, Yansong, Wang, Zining, Cai, Yingfeng, Chen, Long, Li, Yicheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 2D object detection; Accuracy; Algorithms; autonomous driving; complex traffic conditions; Convolution; Detection algorithms; Feature extraction; Formability; Head; image recognition; Object detection; Occlusion; Parameterization; Proposals; Pyramids; Training
• ISSN: 2471-285X; 2471-285X
• DOI: 10.1109/TETCI.2023.3235381

With the rise in popularity of autonomous driving, the speed and accuracy of surrounding objects' detection by in-vehicle sensing technology is becoming increasingly important for autonomous vehicles. Building on CenterNet, this paper proposes CenterNet-Auto, a new anchor-free detection network for driving scenes that can satisfy the detection speed requirements while ensuring detection accuracy. The network's backbone uses the RepVGG model transformed through structural re-parameterization technology. Features of different scales are fused, and feature pyramids and deformable convolution are added after the backbone to accurately detect objects of different sizes. To solve the occlusion problem in the driving scene, this paper proposes the Average Border Model, which supports locating the object using the boundary feature information. The test results demonstrate that the proposed algorithm outperforms CenterNet regarding speed and accuracy on the BDD dataset. The accuracy reaches 55.6%, and the speed reaches 30 FPS, meeting the speed and accuracy requirements in a driving scene.