Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network
The rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the p...
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Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9955079 |
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| author | Dongmei Shi Hongyu Tang |
| author_facet | Dongmei Shi Hongyu Tang |
| author_sort | Dongmei Shi |
| collection | DOAJ |
| description | The rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the presence of interference signals such as spatiotemporal background mixed signals in the driving monitoring video sequence, the recognition accuracy of small targets such as human eyes is low. In this paper, an improved dual-stream convolutional network is proposed to recognize the safe driving behavior. Based on convolutional neural networks (CNNs), attention mechanism (AM) is integrated into a long short-term memory (LSTM) neural network structure, and the hybrid dual-stream AM-LSTM convolutional network channel is designed. The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. The time stream channel uses a single-shot multibox detector (SSD) algorithm to calculate the adjacent two frames of video sequence for the detection of small objects such as face and eyes. Then, AM-LSTM is used to fuse and classify dual-stream information. The self-built driving behavior video image set is built. ROC, accuracy rate, and loss function experiments are carried out in the FDDB database, VOT100 data set, and self-built video image set, respectively. Compared with CNN, SSD, IDT, and dual-stream recognition methods, the accuracy rate of this method can be improved by at least 1.4%, and the average absolute error in four video sequences can be improved by more than 2%. On the contrary, in the self-built image set, the recognition rate of doze reaches 68.3%, which is higher than other methods. The experimental results show that this method has good recognition accuracy and practical application value. |
| format | Article |
| id | doaj-art-b7dc2e842ad842e4bfc4888b947d2243 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-b7dc2e842ad842e4bfc4888b947d22432025-02-03T01:20:44ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/99550799955079Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory NetworkDongmei Shi0Hongyu Tang1Department of Computer Science and Technology, Suzhou College of Information Technology, Suzhou, ChinaSchool of Electrical and Information, Zhenjiang College, Zhenjiang, ChinaThe rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the presence of interference signals such as spatiotemporal background mixed signals in the driving monitoring video sequence, the recognition accuracy of small targets such as human eyes is low. In this paper, an improved dual-stream convolutional network is proposed to recognize the safe driving behavior. Based on convolutional neural networks (CNNs), attention mechanism (AM) is integrated into a long short-term memory (LSTM) neural network structure, and the hybrid dual-stream AM-LSTM convolutional network channel is designed. The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. The time stream channel uses a single-shot multibox detector (SSD) algorithm to calculate the adjacent two frames of video sequence for the detection of small objects such as face and eyes. Then, AM-LSTM is used to fuse and classify dual-stream information. The self-built driving behavior video image set is built. ROC, accuracy rate, and loss function experiments are carried out in the FDDB database, VOT100 data set, and self-built video image set, respectively. Compared with CNN, SSD, IDT, and dual-stream recognition methods, the accuracy rate of this method can be improved by at least 1.4%, and the average absolute error in four video sequences can be improved by more than 2%. On the contrary, in the self-built image set, the recognition rate of doze reaches 68.3%, which is higher than other methods. The experimental results show that this method has good recognition accuracy and practical application value.http://dx.doi.org/10.1155/2021/9955079 |
| spellingShingle | Dongmei Shi Hongyu Tang Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network Journal of Electrical and Computer Engineering |
| title | Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network |
| title_full | Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network |
| title_fullStr | Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network |
| title_full_unstemmed | Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network |
| title_short | Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network |
| title_sort | research on safe driving evaluation method based on machine vision and long short term memory network |
| url | http://dx.doi.org/10.1155/2021/9955079 |
| work_keys_str_mv | AT dongmeishi researchonsafedrivingevaluationmethodbasedonmachinevisionandlongshorttermmemorynetwork AT hongyutang researchonsafedrivingevaluationmethodbasedonmachinevisionandlongshorttermmemorynetwork |