Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5
The rapid advancement of intelligent assisted driving technology has significantly enhanced transportation convenience in society and contributed to the mitigation of traffic safety hazards. Addressing the potential for drivers to experience abnormal physical conditions during the driving process, a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/6/1138 |
| _version_ | 1797241308142632960 |
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| author | Keming Yao Zhongzhou Wang Fuao Guo Feng Li |
| author_facet | Keming Yao Zhongzhou Wang Fuao Guo Feng Li |
| author_sort | Keming Yao |
| collection | DOAJ |
| description | The rapid advancement of intelligent assisted driving technology has significantly enhanced transportation convenience in society and contributed to the mitigation of traffic safety hazards. Addressing the potential for drivers to experience abnormal physical conditions during the driving process, an enhanced lightweight network model based on YOLOv5 for detecting abnormal facial expressions of drivers is proposed in this paper. Initially, the lightweighting of the YOLOv5 backbone network is achieved by integrating the FasterNet Block, a lightweight module from the FasterNet network, with the C3 module in the main network. This combination forms the C3-faster module. Subsequently, the original convolutional modules in the YOLOv5 model are replaced with the improved GSConvns module to reduce computational load. Building upon the GSConvns module, the VoV-GSCSP module is constructed to ensure the lightweighting of the neck network while maintaining detection accuracy. Finally, channel pruning and fine-tuning operations are applied to the entire model. Channel pruning involves removing channels with minimal impact on output results, further reducing the model’s computational load, parameters, and size. The fine-tuning operation compensates for any potential loss in detection accuracy. Experimental results demonstrate that the proposed model achieves a substantial reduction in both parameter count and computational load while maintaining a high detection accuracy of 84.5%. The improved model has a compact size of only 4.6 MB, making it more conducive to the efficient operation of onboard computers. |
| first_indexed | 2024-04-24T18:21:15Z |
| format | Article |
| id | doaj.art-6638df5eabf34195bb54ec31dce7678a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-24T18:21:15Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6638df5eabf34195bb54ec31dce7678a2024-03-27T13:35:05ZengMDPI AGElectronics2079-92922024-03-01136113810.3390/electronics13061138Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5Keming Yao0Zhongzhou Wang1Fuao Guo2Feng Li3College of Electrical Information Engineering, Jiangsu University of Technology, Changzhou 213000, ChinaCollege of Electrical Information Engineering, Jiangsu University of Technology, Changzhou 213000, ChinaCollege of Electrical Information Engineering, Jiangsu University of Technology, Changzhou 213000, ChinaCollege of Electrical Information Engineering, Jiangsu University of Technology, Changzhou 213000, ChinaThe rapid advancement of intelligent assisted driving technology has significantly enhanced transportation convenience in society and contributed to the mitigation of traffic safety hazards. Addressing the potential for drivers to experience abnormal physical conditions during the driving process, an enhanced lightweight network model based on YOLOv5 for detecting abnormal facial expressions of drivers is proposed in this paper. Initially, the lightweighting of the YOLOv5 backbone network is achieved by integrating the FasterNet Block, a lightweight module from the FasterNet network, with the C3 module in the main network. This combination forms the C3-faster module. Subsequently, the original convolutional modules in the YOLOv5 model are replaced with the improved GSConvns module to reduce computational load. Building upon the GSConvns module, the VoV-GSCSP module is constructed to ensure the lightweighting of the neck network while maintaining detection accuracy. Finally, channel pruning and fine-tuning operations are applied to the entire model. Channel pruning involves removing channels with minimal impact on output results, further reducing the model’s computational load, parameters, and size. The fine-tuning operation compensates for any potential loss in detection accuracy. Experimental results demonstrate that the proposed model achieves a substantial reduction in both parameter count and computational load while maintaining a high detection accuracy of 84.5%. The improved model has a compact size of only 4.6 MB, making it more conducive to the efficient operation of onboard computers.https://www.mdpi.com/2079-9292/13/6/1138YOLOv5lightweightingfacial emotion recognitionmodel pruning |
| spellingShingle | Keming Yao Zhongzhou Wang Fuao Guo Feng Li Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5 Electronics YOLOv5 lightweighting facial emotion recognition model pruning |
| title | Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5 |
| title_full | Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5 |
| title_fullStr | Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5 |
| title_full_unstemmed | Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5 |
| title_short | Driver Abnormal Expression Detection Method Based on Improved Lightweight YOLOv5 |
| title_sort | driver abnormal expression detection method based on improved lightweight yolov5 |
| topic | YOLOv5 lightweighting facial emotion recognition model pruning |
| url | https://www.mdpi.com/2079-9292/13/6/1138 |
| work_keys_str_mv | AT kemingyao driverabnormalexpressiondetectionmethodbasedonimprovedlightweightyolov5 AT zhongzhouwang driverabnormalexpressiondetectionmethodbasedonimprovedlightweightyolov5 AT fuaoguo driverabnormalexpressiondetectionmethodbasedonimprovedlightweightyolov5 AT fengli driverabnormalexpressiondetectionmethodbasedonimprovedlightweightyolov5 |