Research on Vehicle Object Detection Algorithm Based on Improved YOLOv3 Algorithm
Vehicle object detection is one of the important research directions in the field of computer vision. Aiming at solving the problems of low accuracy, slow speed, and unsatisfactory results of using traditional methods to detect the object of the vehicle in front of the driverless car on the road, th...
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| Published in: | Journal of physics. Conference series Vol. 1575; no. 1; pp. 12150 - 12158 |
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| Language: | English |
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | Vehicle object detection is one of the important research directions in the field of computer vision. Aiming at solving the problems of low accuracy, slow speed, and unsatisfactory results of using traditional methods to detect the object of the vehicle in front of the driverless car on the road, this paper proposes an improved YOLOv3 vehicle target detection algorithm which we name it F-YOLOv3. First the multi-scale prediction network model is improved according to actual traffic conditions and efficiency requirements based on the original general object detection YOLOv3 algorithm. Then a scale prediction layer is added to improve the detection accuracy of large vehicles and improved k-means++ the algorithm is used to improve the effect of anchor box dimensional clustering and the detection speed. At last an experiment was conducted on a self-made dataset and compared with YOLOv3 in order to test the effectiveness of the F-YOLOv3 algorithm. The test results show that the improved F-YOLOv3 model has a precision mAP of 91.12% and a speed of 59FPS, which are better than the traditional general object detection YOLOv3 algorithm. Therefore, the algorithm has better performance and popularization prospect in vehicle object detection. |
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| AbstractList | Vehicle object detection is one of the important research directions in the field of computer vision. Aiming at solving the problems of low accuracy, slow speed, and unsatisfactory results of using traditional methods to detect the object of the vehicle in front of the driverless car on the road, this paper proposes an improved YOLOv3 vehicle target detection algorithm which we name it F-YOLOv3. First the multi-scale prediction network model is improved according to actual traffic conditions and efficiency requirements based on the original general object detection YOLOv3 algorithm. Then a scale prediction layer is added to improve the detection accuracy of large vehicles and improved k-means++ the algorithm is used to improve the effect of anchor box dimensional clustering and the detection speed. At last an experiment was conducted on a self-made dataset and compared with YOLOv3 in order to test the effectiveness of the F-YOLOv3 algorithm. The test results show that the improved F-YOLOv3 model has a precision mAP of 91.12% and a speed of 59FPS, which are better than the traditional general object detection YOLOv3 algorithm. Therefore, the algorithm has better performance and popularization prospect in vehicle object detection. |
| Author | Liu, Jin Zhang, Dongquan |
| Author_xml | – sequence: 1 givenname: Jin surname: Liu fullname: Liu, Jin email: 18121410@bjtu.edu.cn organization: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University , China – sequence: 2 givenname: Dongquan surname: Zhang fullname: Zhang, Dongquan email: 18121410@bjtu.edu.cn organization: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University , China |
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| CitedBy_id | crossref_primary_10_1038_s41598_023_43458_3 crossref_primary_10_17208_jkpa_2021_04_56_2_79 crossref_primary_10_2478_amns_2024_0322 crossref_primary_10_1155_2022_4260543 crossref_primary_10_3390_app142311175 |
| Cites_doi | 10.1109/CVPR.2017.106 10.1007/s12555-014-0229-7 10.1109/TPAMI.2016.2577031 10.1016/j.eswa.2015.01.032 10.1109/CVPR.2016.91 |
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| DOI | 10.1088/1742-6596/1575/1/012150 |
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| References | Van Pham (JPCS_1575_1_012150bib1) 2015; 13 Hassannejad (JPCS_1575_1_012150bib2) 2015; 42 Girshick (JPCS_1575_1_012150bib4) 2014 Redmon (JPCS_1575_1_012150bib5) Redmon (JPCS_1575_1_012150bib6) 2017 Redmon (JPCS_1575_1_012150bib7) Arthur (JPCS_1575_1_012150bib9) Lin (JPCS_1575_1_012150bib8) Ren (JPCS_1575_1_012150bib3) 2017; 39 |
| References_xml | – ident: JPCS_1575_1_012150bib8 article-title: Feature Pyramid Networks for Object Detection doi: 10.1109/CVPR.2017.106 – ident: JPCS_1575_1_012150bib7 article-title: YOLOv3: An Incremental Improvement – volume: 13 start-page: 1150 year: 2015 ident: JPCS_1575_1_012150bib1 article-title: Front-view car detection and counting with occlusion in dense traffic flow publication-title: Int J Control Autom Syst. doi: 10.1007/s12555-014-0229-7 – volume: 39 start-page: 1137 year: 2017 ident: JPCS_1575_1_012150bib3 article-title: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks publication-title: IEEE Trans Pattern Anal Mach Intell. doi: 10.1109/TPAMI.2016.2577031 – start-page: 580 year: 2014 ident: JPCS_1575_1_012150bib4 article-title: Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation – start-page: 6517 year: 2017 ident: JPCS_1575_1_012150bib6 article-title: YOLO9000: Better, Faster, Stronger – start-page: 9 ident: JPCS_1575_1_012150bib9 article-title: k-means++: The Advantages of Careful Seeding – volume: 42 start-page: 4167 year: 2015 ident: JPCS_1575_1_012150bib2 article-title: Detection of moving objects in roundabouts based on a monocular system publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.01.032 – ident: JPCS_1575_1_012150bib5 article-title: You Only Look Once: Unified, Real-Time Object Detection doi: 10.1109/CVPR.2016.91 |
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| SubjectTerms | Accuracy Algorithms Autonomous cars Clustering Computer vision Driving conditions Object recognition Physics Target detection Traffic |
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| Title | Research on Vehicle Object Detection Algorithm Based on Improved YOLOv3 Algorithm |
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