SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection
Open accessAbstract
Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN-based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects and 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.
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Advanced CNN has achieved good results in object detection, but CNN is sensitive to scale changes in object detection.
“Advanced CNN has achieved good results in object detection; however, CNN is sensitive to scale changes in object detection [17,18].”
From Vision-based vehicle detection and counting system using deep learning in highway scenes (2019) · cites this paper as [18]
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- Authors
- Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., & Heng, P.-A.
- Affiliations
- South China University of Technology (CN)
- Chinese University of Hong Kong (HK)
- Hong Kong Polytechnic University (HK)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2019-03
- Volume
- 20
- Issue
- 3
- Pages
- 1010-1019
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2795812085
- Cited by
- 341
- References
- 67
- Citations by year
- 2026: 16 · 2025: 39 · 2024: 39 · 2023: 55 · 2022: 56 · 2021: 51 · 2020: 45 · 2019: 38 · 2018: 2
- FWCI
- 17.02
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61772206, U1611461, 61472145, 61702194)
- Special Fund of Science and Technology Research and Development on Application from Guangdong Province (2016B010124011, 2016B010127003)
- Guangdong High-level Personnel of Special Support Program (2016TQ03X319)
- Natural Science Foundation of Guangdong Province (2017A030311027, 2017A030312008)
- Major Project in Industrial Technology in Guangzhou (2018-0601-ZB-0271)
- Hong Kong Polytechnic University (1-ZE8J)
- Hong Kong Ph.D. Fellowship
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- vehicle detection, scale variance, small object detection, traffic images, KITTI dataset, real-time detection
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