Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5
With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/21/8577 |
| _version_ | 1797466455389765632 |
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| author | Jiale Yao Xiangsuo Fan Bing Li Wenlin Qin |
| author_facet | Jiale Yao Xiangsuo Fan Bing Li Wenlin Qin |
| author_sort | Jiale Yao |
| collection | DOAJ |
| description | With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86. |
| first_indexed | 2024-03-09T18:39:08Z |
| format | Article |
| id | doaj.art-6bd2ef9a431c4a89a036dd48ae86f0f8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:39:08Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6bd2ef9a431c4a89a036dd48ae86f0f82023-11-24T06:50:16ZengMDPI AGSensors1424-82202022-11-012221857710.3390/s22218577Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5Jiale Yao0Xiangsuo Fan1Bing Li2Wenlin Qin3College of Automation, Guangxi University of Science and Technology, Liuzhou 545006, ChinaCollege of Automation, Guangxi University of Science and Technology, Liuzhou 545006, ChinaGuangxi Collaborative Innovation Centre for Earthmoving Machinery, Guangxi University of Science and Technology, Liuzhou 545006, ChinaCollege of Automation, Guangxi University of Science and Technology, Liuzhou 545006, ChinaWith the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.https://www.mdpi.com/1424-8220/22/21/8577adverse weatheradaptive color levelsYOLOv5transformerCBAM |
| spellingShingle | Jiale Yao Xiangsuo Fan Bing Li Wenlin Qin Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5 Sensors adverse weather adaptive color levels YOLOv5 transformer CBAM |
| title | Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5 |
| title_full | Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5 |
| title_fullStr | Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5 |
| title_full_unstemmed | Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5 |
| title_short | Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5 |
| title_sort | adverse weather target detection algorithm based on adaptive color levels and improved yolov5 |
| topic | adverse weather adaptive color levels YOLOv5 transformer CBAM |
| url | https://www.mdpi.com/1424-8220/22/21/8577 |
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