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YOLOv7-RAR for Urban Vehicle Detection
Open accessAbstract
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 [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]
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“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]
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Metadata
- Authors
- Zhang, Y., Sun, Y., Wang, Z., & Jiang, Y.
- Affiliations
- Nanjing Forestry University (CN)
- Corresponding author
- Jiang, Ying
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-02-06
- Volume
- 23
- Issue
- 4
- Pages
- 1801
- ISSN
- 1424-8220 (ISSN-L)
- 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
- gold
- doi.org
- 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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