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Cited by 49Advanced Neural Network ApplicationsAutonomous Vehicle Technology and SafetyDomain Adaptation and Few-Shot Learning

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Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model

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Li, Yanyi, Wang, Jian, Huang, Jin, Li, Yuping·2022·Sensors
DOI 10.3390/s22103783PMC PMC9143950
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Abstract

With the introduction of concepts such as ubiquitous mapping, mapping-related technologies are gradually applied in autonomous driving and target recognition. There are many problems in vision measurement and remote sensing, such as difficulty in automatic vehicle discrimination, high missing rates under multiple vehicle targets, and sensitivity to the external environment. This paper proposes an improved RES-YOLO detection algorithm to solve these problems and applies it to the automatic detection of vehicle targets. Specifically, this paper improves the detection effect of the traditional YOLO algorithm by selecting optimized feature networks and constructing adaptive loss functions. The BDD100K data set was used for training and verification. Additionally, the optimized YOLO deep learning vehicle detection model is obtained and compared with recent advanced target recognition algorithms. Experimental results show that the proposed algorithm can automatically identify multiple vehicle targets effectively and can significantly reduce missing and false rates, with the local optimal accuracy of up to 95% and the average accuracy above 86% under large data volume detection. The average accuracy of our algorithm is higher than all five other algorithms including the latest SSD and Faster-RCNN. In average accuracy, the RES-YOLO algorithm for small data volume and large data volume is 1.0% and 1.7% higher than the original YOLO. In addition, the training time is shortened by 7.3% compared with the original algorithm. The network is then tested with five types of local measured vehicle data sets and shows satisfactory recognition accuracy under different interference backgrounds. In short, the method in this paper can complete the task of vehicle target detection under different environmental interferences.

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Figures

Figure A1 RES-YOLO Network Structure.
Figure A1 RES-YOLO Network Structure.CC BY · PubMed Central
Figure 1 The object position.
Figure 1 The object position.CC BY · PubMed Central
Figure 2 YOLO network structure.
Figure 2 YOLO network structure.CC BY · PubMed Central
Figure 3 Schematic diagram of RES-YOLO network structure.
Figure 3 Schematic diagram of RES-YOLO network structure.CC BY · PubMed Central
Figure 4 Data enhancement.
Figure 4 Data enhancement.CC BY · PubMed Central
Figure 5 Derivative variation of loss function. (a) Positive sample; (b) Negative sample.
Figure 5 Derivative variation of loss function. (a) Positive sample; (b) Negative sample.CC BY · PubMed Central
Figure 6 Custom data set in this research.
Figure 6 Custom data set in this research.CC BY · PubMed Central
Figure 7 The visual sensor structure used in this paper.
Figure 7 The visual sensor structure used in this paper.CC BY · PubMed Central
Figure 8 Accuracy–loss curve in this research. (a) Lightweight network; (b) Robust network; (c) Complex network.
Figure 8 Accuracy–loss curve in this research. (a) Lightweight network; (b) Robust network; (c) Complex network.CC BY · PubMed Central
Figure 9 Precision and recall (PR) curve in this research. (a) Lightweight network; (b) Robust network; (c) Complex network.
Figure 9 Precision and recall (PR) curve in this research. (a) Lightweight network; (b) Robust network; (c) Complex network.CC BY · PubMed Central
Figure 10 Comparison diagram of preliminary test of small amount of data on network performance.
Figure 10 Comparison diagram of preliminary test of small amount of data on network performance.CC BY · PubMed Central
Figure 11 The method flow chart of this paper. The data processing flow includes three parts: (1) data set preprocessing and data enhancement, (2) initialization of the YOLO training network, and (3) vehicle identification network training and testing.
Figure 11 The method flow chart of this paper. The data processing flow includes three parts: (1) data set preprocessing and data enhancement, (2) initialization of the YOLO training network, and (3) vehicle identification network training and testing.CC BY · PubMed Central
Figure 12 Precision and recall (PR) curve in small data set (small amount of data) [20,21,22].
Figure 12 Precision and recall (PR) curve in small data set (small amount of data) [20,21,22].CC BY · PubMed Central
Figure 13 Accuracy–loss curve in small data set (small amount of data) [20,21,22].
Figure 13 Accuracy–loss curve in small data set (small amount of data) [20,21,22].CC BY · PubMed Central
Figure 14 Precision and recall (PR) curve in small data set (large amount of data) [21].
Figure 14 Precision and recall (PR) curve in small data set (large amount of data) [21].CC BY · PubMed Central
Figure 15 Accuracy–loss curve in small data set (large amount of data) [21].
Figure 15 Accuracy–loss curve in small data set (large amount of data) [21].CC BY · PubMed Central
Figure 16 Actual test results. (a) Sample data 1 [21]; (b) Sample data 2 [21]; (c) Sample data 3 [21].
Figure 16 Actual test results. (a) Sample data 1 [21]; (b) Sample data 2 [21]; (c) Sample data 3 [21].CC BY · PubMed Central
Figure 17 Actual test renderings of six types of networks [20,21,22].
Figure 17 Actual test renderings of six types of networks [20,21,22].CC BY · PubMed Central
Figure 18 Actual test renderings of five types of networks.
Figure 18 Actual test renderings of five types of networks.CC BY · PubMed Central
Figure 19 Effect diagram of actual test of RES-YOLO network in local data. (a) Urban road environment; (b) Tunnel road environment; (c) Skyway environment; (d) Night dark environment; (e) Trees, pedestrians, and other disturbing environments.
Figure 19 Effect diagram of actual test of RES-YOLO network in local data. (a) Urban road environment; (b) Tunnel road environment; (c) Skyway environment; (d) Night dark environment; (e) Trees, pedestrians, and other disturbing environments.CC BY · PubMed Central
Figure 20 Main interface of vehicle detection system.
Figure 20 Main interface of vehicle detection system.CC BY · PubMed Central

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References 37 · Cited by 49

Research works printed but not linked: 33

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Metadata

Authors
Li, Y., Wang, J., Huang, J., & Li, Y.
Affiliations
  • Tongji University (CN)
  • Shandong University of Science and Technology (CN)
  • Zhejiang University (CN)
Corresponding author
Wang, Jian
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-05-16
Volume
22
Issue
10
Pages
3783
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22103783
PMC
PMC9143950
OpenAlex
W4280511929
Cited by
49
References
37
Citations by year
2026: 3 · 2025: 13 · 2024: 13 · 2023: 18 · 2022: 2
FWCI
3.76
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)
Funders
  • introduction plan of high-end foreign experts (G2021025006L, 2019JZZY020103)
  • major scientific and technological innovation project of Shandong Province, China (G2021025006L, 2019JZZY020103)
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
vehicle detection, YOLO, object detection, adaptive loss function, BDD100K, autonomous driving, false positive reduction
MeSH terms
Deep Learning, Algorithms

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CoChat. (2026). Citation check: Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model. https://app.cochat.ai/papers/10.3390/s22103783
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Li, Y., Wang, J., Huang, J., & Li, Y. (2022). Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model. Sensors. https://doi.org/10.3390/s22103783