Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection
Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand th...
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| Format: | Article |
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MDPI AG
2019-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/18/3753 |
| _version_ | 1818573783879909376 |
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| author | Zhenzhou Wang Wei Huo Pingping Yu Lin Qi Shanshan Geng Ning Cao |
| author_facet | Zhenzhou Wang Wei Huo Pingping Yu Lin Qi Shanshan Geng Ning Cao |
| author_sort | Zhenzhou Wang |
| collection | DOAJ |
| description | Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference. |
| first_indexed | 2024-12-15T00:17:31Z |
| format | Article |
| id | doaj.art-34c0c7ced33c495dbf5672b93d5e2baf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-15T00:17:31Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-34c0c7ced33c495dbf5672b93d5e2baf2022-12-21T22:42:25ZengMDPI AGApplied Sciences2076-34172019-09-01918375310.3390/app9183753app9183753Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight DetectionZhenzhou Wang0Wei Huo1Pingping Yu2Lin Qi3Shanshan Geng4Ning Cao5School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, ChinaSchool of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, ChinaSchool of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, ChinaIncreasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference.https://www.mdpi.com/2076-3417/9/18/3753driving intention analysisvehicle detectiontaillight detectiontaillight semantic recognition |
| spellingShingle | Zhenzhou Wang Wei Huo Pingping Yu Lin Qi Shanshan Geng Ning Cao Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection Applied Sciences driving intention analysis vehicle detection taillight detection taillight semantic recognition |
| title | Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection |
| title_full | Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection |
| title_fullStr | Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection |
| title_full_unstemmed | Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection |
| title_short | Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection |
| title_sort | performance evaluation of region based convolutional neural networks toward improved vehicle taillight detection |
| topic | driving intention analysis vehicle detection taillight detection taillight semantic recognition |
| url | https://www.mdpi.com/2076-3417/9/18/3753 |
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