Accurate Single Stage Detector Using Recurrent Rolling Convolution
Open accessAbstract
Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are deep in context. We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.
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Single-stage detectors find applications in various domains, including autonomous vehicles, surveillance, robotics, and object recognition in images and videos, and their speed and simplicity make them well-suited for real-time detection scenarios.
“Single-stage detectors find applications in various domains, including autonomous vehicles, surveillance, robotics, and object recognition in images and videos. Their speed and simplicity make them particularly well-suited for scenarios where real-time detection is crucial. These applications are supported by numerous studies that highlight the efficiency and effectiveness of single-stage detec”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [28]
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- Authors
- Ren, J., Chen, X., Liu, J., Sun, W., Pang, J., Yan, Q., Tai, Y.-W., & Xu, L.
- Affiliations
- Sensetime (China) (CN)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 752-760
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2609144136
- Cited by
- 305
- References
- 5
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 21 · 2023: 21 · 2022: 36 · 2021: 36 · 2020: 51 · 2019: 65 · 2018: 48 · 2017: 9
- FWCI
- 13.83
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- one-stage object detection, multi-scale feature maps, bounding box regression, object classification, KITTI dataset, car detection, cyclist detection, pedestrian detection
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