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Cited by 123Advanced Neural Network ApplicationsAutonomous Vehicle Technology and SafetyDomain Adaptation and Few-Shot Learning

YOLOv7-RAR for Urban Vehicle Detection

Open access
Zhang, Yuan, Sun, Youpeng, Wang, Zheng, Jiang, Ying·2023·Sensors
DOI 10.3390/s23041801PMC PMC9964850
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Abstract

Aiming at the problems of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban roads, weak perception of small targets in perspective, and insufficient feature extraction, the YOLOv7-RAR recognition algorithm is proposed. The algorithm is improved from the following three directions based on YOLOv7. Firstly, in view of the insufficient nonlinear feature fusion of the original backbone network, the Res3Unit structure is used to reconstruct the backbone network of YOLOv7 to improve the ability of the network model architecture to obtain more nonlinear features. Secondly, in view of the problem that there are many interference backgrounds in urban roads and that the original network is weak in positioning targets such as vehicles, a plug-and-play hybrid attention mechanism module, ACmix, is added after the SPPCSPC layer of the backbone network to enhance the network’s attention to vehicles and reduce the interference of other targets. Finally, aiming at the problem that the receptive field of the original network Narrows, with the deepening of the network model, leads to a high miss rate of small targets, the Gaussian receptive field scheme used in the RFLA (Gaussian-receptive-field-based label assignment) module is used at the connection between the feature fusion area and the detection head to improve the receptive field of the network model for small objects in the image. Combining the three improvement measures, the first letter of the name of each improvement measure is selected, and the improved algorithm is named the YOLOv7-RAR algorithm. Experiments show that on urban roads with crowded vehicles and different weather patterns, the average detection accuracy of the YOLOv7-RAR algorithm reaches 95.1%, which is 2.4% higher than that of the original algorithm; the AP50:90 performance is 12.6% higher than that of the original algorithm. The running speed of the YOLOv7-RAR algorithm reaches 96 FPS, which meets the real-time requirements of vehicle detection; hence, the algorithm can be better applied to vehicle detection.

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  • YOLOv7 with ACmix, RFLA, and Res3Unit backbone achieved 89.2% AP on road traffic data.

    “| YOLOv7 [80] | AP | Proposed modifications including ACmix for hybrid attention, RFLA for feature fusion, and Res3Unit backbone, achieving 89.2% AP. | Road traffic data |”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [80]

    Section 4.2.1 (page not provided) says the YOLOv7-RAR model combines Res3Unit, ACmix, and RFLA, and Section 4.2.6 evaluates it for urban traffic detection. The copy does not report the claimed 89.2% AP; it reports a 1.6% AP improvement and, in the Abstract, 95.1% average detection accuracy. It describes RFLA as improving the receptive field for small targets, rather than as a feature-fusion method. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • YOLOv7 has been enhanced with a hybrid attention mechanism (ACmix) and the Res3Unit backbone, significantly improving its performance, achieving an AP score of 90.8% on road traffic data.

    “For instance, YOLOv7 has been enhanced with a hybrid attention mechanism (ACmix) and the Res3Unit backbone, significantly improving its performance, achieving an AP score of 90.8% on road traffic data [80].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [80]

    The Introduction and §4.2 describe a YOLOv7 model improved with a Res3Unit backbone and ACmix attention mechanism, and report improved performance for urban vehicle detection. The cited 90.8% AP score is not supported by our copy; its Abstract instead reports 95.1% average detection accuracy for YOLOv7-RAR. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 8

  1. [15]He K. Gkioxari G. Dollár P. Girshick R. Mask r-cnn Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV) Venice, Italy 22–29 October 2017 2961 2969
  2. [17]Bharati P. Pramanik A. Deep learning techniques—R-cnn to mask r-cnn: A survey Computational Intelligence in Pattern Recognition Springer Singapore 2020 657 668
  3. [18]Girshick R. Fast r-cnn Proceedings of the 2015 IEEE International Conference on Computer Vision Santiago, Chile 7–13 December 2015 1440 1448
  4. [33]Liu W. Anguelov D. Erhan D. Szegedy C. Reed S. Fu C.-Y. Berg A.C. Ssd: Single shot multibox detector European Conference on Computer Vision Springer Berlin/Heidelberg, Germany 2016 21 37
  5. [35]Wang C.Y. Bochkovskiy A. Liao H.Y.M. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors arXiv 2022 2207.02696
  6. [42]Xu C. Tao D. Xu C. A survey on multi-view learning arXiv 2013 1304.5634
  7. [43]Yang F. Zhang X. Liu B. Video object tracking based on yolov7 and deepsort arXiv 2022 2207.12202
  8. [45]Carion N. Massa F. Synnaeve G. Usunier N. Kirillov A. Zagoruyko S. End-to-end object detection with transformers Computer Vision–ECCV 2020 Springer Berlin/Heidelberg, Germany 2020 213 229

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Related

Authors
Zhang, Y., Sun, Y., Wang, Z., & Jiang, Y.
Affiliations
Nanjing Forestry University (CN)
Corresponding author
Jiang, Ying
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2023-02-06
Volume
23
Issue
4
Pages
1801
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s23041801
PMC
PMC9964850
OpenAlex
W4319319913
Cited by
119
References
37
Citations by year
2026: 12 · 2025: 30 · 2024: 42 · 2023: 35
FWCI
10.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
YOLOv7, vehicle detection, urban streets, small object detection, hybrid attention mechanism, label assignment, real-time object detection, crowded scenes

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