Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net
The whistle of the rail train is usually directly controlled by the driver. However, in long-distance transportation, there is a risk of traffic accidents due to driver fatigue or distraction. In addition, the noise pollution of the train whistle has also been criticized. In order to solve the above...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1695 |
| _version_ | 1797625080311709696 |
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| author | Kai Wang Zhonghang Zhang Chaozhi Cai Jianhua Ren Nan Zhang |
| author_facet | Kai Wang Zhonghang Zhang Chaozhi Cai Jianhua Ren Nan Zhang |
| author_sort | Kai Wang |
| collection | DOAJ |
| description | The whistle of the rail train is usually directly controlled by the driver. However, in long-distance transportation, there is a risk of traffic accidents due to driver fatigue or distraction. In addition, the noise pollution of the train whistle has also been criticized. In order to solve the above two problems, an intelligent whistling system for railway trains based on deep learning is proposed. The system judges whether to whistle and intelligently adjusts the volume of the whistle according to the road conditions of the train. The system consists of a road condition sensing module and a whistling decision module. The former includes the target detection model based on YOLOv4 and the semantic segmentation model based on U-Net, which can extract the key information of the road conditions ahead; the latter is to carry out logical analysis of the data after the intelligent recognition and processing and make the whistling decision. Based on the train-running data set, the intelligent whistle system model is tested. The results of this research show that the whistling accuracy of the model on the test set is 99.22%, the average volume error is 1.91 dB/time, and the Frames Per Second (FPS) is 18.7 <i>f</i>/<i>s</i>. Therefore, the intelligent whistle system model proposed in this paper has high reliability and is suitable for further development and application in actual scenes. |
| first_indexed | 2024-03-11T09:51:46Z |
| format | Article |
| id | doaj.art-1b6fc69c1ca54f1ba0eb2da0c86ab6e5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:51:46Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1b6fc69c1ca54f1ba0eb2da0c86ab6e52023-11-16T16:09:13ZengMDPI AGApplied Sciences2076-34172023-01-01133169510.3390/app13031695Intelligent Whistling System of Rail Train Based on YOLOv4 and U-NetKai Wang0Zhonghang Zhang1Chaozhi Cai2Jianhua Ren3Nan Zhang4School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaDepartment of Mechanical and Electrical Engineering, Handan Polytechnic College, Handan 056001, ChinaThe whistle of the rail train is usually directly controlled by the driver. However, in long-distance transportation, there is a risk of traffic accidents due to driver fatigue or distraction. In addition, the noise pollution of the train whistle has also been criticized. In order to solve the above two problems, an intelligent whistling system for railway trains based on deep learning is proposed. The system judges whether to whistle and intelligently adjusts the volume of the whistle according to the road conditions of the train. The system consists of a road condition sensing module and a whistling decision module. The former includes the target detection model based on YOLOv4 and the semantic segmentation model based on U-Net, which can extract the key information of the road conditions ahead; the latter is to carry out logical analysis of the data after the intelligent recognition and processing and make the whistling decision. Based on the train-running data set, the intelligent whistle system model is tested. The results of this research show that the whistling accuracy of the model on the test set is 99.22%, the average volume error is 1.91 dB/time, and the Frames Per Second (FPS) is 18.7 <i>f</i>/<i>s</i>. Therefore, the intelligent whistle system model proposed in this paper has high reliability and is suitable for further development and application in actual scenes.https://www.mdpi.com/2076-3417/13/3/1695rail traindeep learningintelligent whistleobject detectionsemantic segmentation |
| spellingShingle | Kai Wang Zhonghang Zhang Chaozhi Cai Jianhua Ren Nan Zhang Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net Applied Sciences rail train deep learning intelligent whistle object detection semantic segmentation |
| title | Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net |
| title_full | Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net |
| title_fullStr | Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net |
| title_full_unstemmed | Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net |
| title_short | Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net |
| title_sort | intelligent whistling system of rail train based on yolov4 and u net |
| topic | rail train deep learning intelligent whistle object detection semantic segmentation |
| url | https://www.mdpi.com/2076-3417/13/3/1695 |
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