Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network
Matching the detection speed and accuracy in practical applications is considered to improve the speed of video object detection in front of trains. Lightweight convolutional neural network MobileNet and clustering ideas are combined to improve the object detection algorithm, and the MYOLO-lite mode...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/4377953 |
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| _version_ | 1849304674760392704 |
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| author | Yang Liu Mengfei Gao Humin Zong Xinping Wang Jinshuang Li |
| author_facet | Yang Liu Mengfei Gao Humin Zong Xinping Wang Jinshuang Li |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Matching the detection speed and accuracy in practical applications is considered to improve the speed of video object detection in front of trains. Lightweight convolutional neural network MobileNet and clustering ideas are combined to improve the object detection algorithm, and the MYOLO-lite model object detection algorithm is designed. The self-made object dataset of a forward-moving train is combined with the K-means clustering idea. The transcendental frame is redesigned to enhance scale adaptability. The nonmaximum suppression and loss function improvement methods of the MYOLO-lite network model are proposed for occluded tracks in front of the train, obstruction of trains, low detection accuracy of large object coincidence, and uneven distribution of positive and negative samples. The mean average precision value of the experimentally designed MYOLO-lite model object detection algorithm reaches 95.74%, with 42.04 frames per second. Detecting a picture only takes 0.024 s. |
| format | Article |
| id | doaj-art-41eca192223b48669bf99b8a74925eb9 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-41eca192223b48669bf99b8a74925eb92025-08-20T03:55:40ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/4377953Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural NetworkYang Liu0Mengfei Gao1Humin Zong2Xinping Wang3Jinshuang Li4School of Automation and Electrical EngineeringSchool of Automation and Electrical EngineeringNational Marine Environmental Monitoring CenterSchool of Automation and Electrical EngineeringSchool of Automation and Electrical EngineeringMatching the detection speed and accuracy in practical applications is considered to improve the speed of video object detection in front of trains. Lightweight convolutional neural network MobileNet and clustering ideas are combined to improve the object detection algorithm, and the MYOLO-lite model object detection algorithm is designed. The self-made object dataset of a forward-moving train is combined with the K-means clustering idea. The transcendental frame is redesigned to enhance scale adaptability. The nonmaximum suppression and loss function improvement methods of the MYOLO-lite network model are proposed for occluded tracks in front of the train, obstruction of trains, low detection accuracy of large object coincidence, and uneven distribution of positive and negative samples. The mean average precision value of the experimentally designed MYOLO-lite model object detection algorithm reaches 95.74%, with 42.04 frames per second. Detecting a picture only takes 0.024 s.http://dx.doi.org/10.1155/2022/4377953 |
| spellingShingle | Yang Liu Mengfei Gao Humin Zong Xinping Wang Jinshuang Li Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network Journal of Advanced Transportation |
| title | Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network |
| title_full | Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network |
| title_fullStr | Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network |
| title_full_unstemmed | Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network |
| title_short | Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network |
| title_sort | real time object detection for the running train based on the improved yolo v4 neural network |
| url | http://dx.doi.org/10.1155/2022/4377953 |
| work_keys_str_mv | AT yangliu realtimeobjectdetectionfortherunningtrainbasedontheimprovedyolov4neuralnetwork AT mengfeigao realtimeobjectdetectionfortherunningtrainbasedontheimprovedyolov4neuralnetwork AT huminzong realtimeobjectdetectionfortherunningtrainbasedontheimprovedyolov4neuralnetwork AT xinpingwang realtimeobjectdetectionfortherunningtrainbasedontheimprovedyolov4neuralnetwork AT jinshuangli realtimeobjectdetectionfortherunningtrainbasedontheimprovedyolov4neuralnetwork |