The DOI matches its Crossref record; 65 of the 67 references it cites were found in a registry or an index.
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; 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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Deep Residual Learning for Image Recognition
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Research works printed but not linked: 1
- sermanet, 2013, Overfeat Integrated Recognition Localization and Detection Using Convolutional Networks
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Sorted by citations
Towards Large-Scale Small Object Detection: Survey and Benchmarks
Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., & Han, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3290594 · Cited by 683
Computer Vision for Autonomous Vehicles
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000079 · Cited by 707
Choi, J., Chun, D., Kim, H., & Lee, H.-J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00059 · Cited by 505
Depth-Attentional Features for Single-Image Rain Removal
Hu, X., Fu, C., Zhu, L., & Heng, P. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00821 · Cited by 417
Self-citationDeep learning for unmanned aerial vehicle-based object detection and tracking: a survey
Wu, X., Li, W., Hong, D., Tao, R., & Du, Q. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3115137 · Cited by 421
Metadata
- 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
- 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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