YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment
In practice, the object detection algorithm is limited by a complex detection environment, hardware costs, computing power, and chip running memory. The performance of the detector will be greatly reduced during operation. Determining how to realize real-time, fast, and high-precision pedestrian rec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5539 |
| _version_ | 1797592703653904384 |
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| author | Xinchao Liu Yier Lin |
| author_facet | Xinchao Liu Yier Lin |
| author_sort | Xinchao Liu |
| collection | DOAJ |
| description | In practice, the object detection algorithm is limited by a complex detection environment, hardware costs, computing power, and chip running memory. The performance of the detector will be greatly reduced during operation. Determining how to realize real-time, fast, and high-precision pedestrian recognition in a foggy traffic environment is a very challenging problem. To solve this problem, the dark channel de-fogging algorithm is added to the basis of the YOLOv7 algorithm, which effectively improves the de-fogging efficiency of the dark channel through the methods of down-sampling and up-sampling. In order to further improve the accuracy of the YOLOv7 object detection algorithm, the ECA module and a detection head are added to the network to improve object classification and regression. Moreover, an 864 × 864 network input size is used for model training to improve the accuracy of the object detection algorithm for pedestrian recognition. Then the combined pruning strategy was used to improve the optimized YOLOv7 detection model, and finally, the optimization algorithm YOLO-GW was obtained. Compared with YOLOv7 object detection, YOLO-GW increased Frames Per Second (FPS) by 63.08%, mean Average Precision (mAP) increased by 9.06%, parameters decreased by 97.66%, and volume decreased by 96.36%. Smaller training parameters and model space make it possible for the YOLO-GW target detection algorithm to be deployed on the chip. Through analysis and comparison of experimental data, it is concluded that YOLO-GW is more suitable for pedestrian detection in a fog environment than YOLOv7. |
| first_indexed | 2024-03-11T01:56:54Z |
| format | Article |
| id | doaj.art-49708e06fff1444a95ea9624b673bc96 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:56:54Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-49708e06fff1444a95ea9624b673bc962023-11-18T12:32:32ZengMDPI AGSensors1424-82202023-06-012312553910.3390/s23125539YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic EnvironmentXinchao Liu0Yier Lin1College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, ChinaCollege of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, ChinaIn practice, the object detection algorithm is limited by a complex detection environment, hardware costs, computing power, and chip running memory. The performance of the detector will be greatly reduced during operation. Determining how to realize real-time, fast, and high-precision pedestrian recognition in a foggy traffic environment is a very challenging problem. To solve this problem, the dark channel de-fogging algorithm is added to the basis of the YOLOv7 algorithm, which effectively improves the de-fogging efficiency of the dark channel through the methods of down-sampling and up-sampling. In order to further improve the accuracy of the YOLOv7 object detection algorithm, the ECA module and a detection head are added to the network to improve object classification and regression. Moreover, an 864 × 864 network input size is used for model training to improve the accuracy of the object detection algorithm for pedestrian recognition. Then the combined pruning strategy was used to improve the optimized YOLOv7 detection model, and finally, the optimization algorithm YOLO-GW was obtained. Compared with YOLOv7 object detection, YOLO-GW increased Frames Per Second (FPS) by 63.08%, mean Average Precision (mAP) increased by 9.06%, parameters decreased by 97.66%, and volume decreased by 96.36%. Smaller training parameters and model space make it possible for the YOLO-GW target detection algorithm to be deployed on the chip. Through analysis and comparison of experimental data, it is concluded that YOLO-GW is more suitable for pedestrian detection in a fog environment than YOLOv7.https://www.mdpi.com/1424-8220/23/12/5539YOLO-GWpedestrian detectionfoggy weatherdark channel de-foggingmodel pruningECA module |
| spellingShingle | Xinchao Liu Yier Lin YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment Sensors YOLO-GW pedestrian detection foggy weather dark channel de-fogging model pruning ECA module |
| title | YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment |
| title_full | YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment |
| title_fullStr | YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment |
| title_full_unstemmed | YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment |
| title_short | YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment |
| title_sort | yolo gw quickly and accurately detecting pedestrians in a foggy traffic environment |
| topic | YOLO-GW pedestrian detection foggy weather dark channel de-fogging model pruning ECA module |
| url | https://www.mdpi.com/1424-8220/23/12/5539 |
| work_keys_str_mv | AT xinchaoliu yologwquicklyandaccuratelydetectingpedestriansinafoggytrafficenvironment AT yierlin yologwquicklyandaccuratelydetectingpedestriansinafoggytrafficenvironment |