Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks
In recent years, illegal passenger transport in freight trucks has become a critical concern for traffic safety and law enforcement. This study proposes an automated system for detecting illegal passenger transport using an improved CLIP-ILP (Illegal Passenger Detection) model. The proposed model in...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10836735/ |
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| author | Xuan Wu Wenlin Pan |
| author_facet | Xuan Wu Wenlin Pan |
| author_sort | Xuan Wu |
| collection | DOAJ |
| description | In recent years, illegal passenger transport in freight trucks has become a critical concern for traffic safety and law enforcement. This study proposes an automated system for detecting illegal passenger transport using an improved CLIP-ILP (Illegal Passenger Detection) model. The proposed model incorporates multi-scale feature fusion, a cross-modal self-attention mechanism, and a more powerful text encoder to enhance detection performance. To evaluate the model, we constructed a comprehensive dataset consisting of tens of thousands of truck images, categorized into two types of trucks (four-wheeled and three-wheeled) and further classified into two subcategories: “illegal passenger transport” and “non-illegal passenger transport.” The model was trained using this dataset, with an emphasis on leveraging the CLIP framework’s ability to understand and integrate visual and textual data. Experimental results demonstrate that the proposed CLIP-ILP model achieves superior accuracy and robustness in detecting illegal passenger transport under various conditions. This research not only highlights the potential of deep learning technologies in enhancing traffic safety but also provides a novel and efficient approach for law enforcement agencies to monitor and address this growing issue effectively. You can access the code for our proposed method at <uri>https://github.com/wu-xuan-git/CLIP-ILP</uri>. |
| format | Article |
| id | doaj-art-1c109c6a27de4c8481f9967780d35987 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1c109c6a27de4c8481f9967780d359872025-01-25T00:02:25ZengIEEEIEEE Access2169-35362025-01-0113142141422410.1109/ACCESS.2025.352800610836735Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight TrucksXuan Wu0https://orcid.org/0009-0005-9386-184XWenlin Pan1https://orcid.org/0009-0006-4752-0849School of Electrical and Information Technology, Yunnan Minzu University, Kunming, ChinaSchool of Mathematics and Computer Science, Yunnan Minzu University, Kunming, ChinaIn recent years, illegal passenger transport in freight trucks has become a critical concern for traffic safety and law enforcement. This study proposes an automated system for detecting illegal passenger transport using an improved CLIP-ILP (Illegal Passenger Detection) model. The proposed model incorporates multi-scale feature fusion, a cross-modal self-attention mechanism, and a more powerful text encoder to enhance detection performance. To evaluate the model, we constructed a comprehensive dataset consisting of tens of thousands of truck images, categorized into two types of trucks (four-wheeled and three-wheeled) and further classified into two subcategories: “illegal passenger transport” and “non-illegal passenger transport.” The model was trained using this dataset, with an emphasis on leveraging the CLIP framework’s ability to understand and integrate visual and textual data. Experimental results demonstrate that the proposed CLIP-ILP model achieves superior accuracy and robustness in detecting illegal passenger transport under various conditions. This research not only highlights the potential of deep learning technologies in enhancing traffic safety but also provides a novel and efficient approach for law enforcement agencies to monitor and address this growing issue effectively. You can access the code for our proposed method at <uri>https://github.com/wu-xuan-git/CLIP-ILP</uri>.https://ieeexplore.ieee.org/document/10836735/Smart transportationCLIPimage classification |
| spellingShingle | Xuan Wu Wenlin Pan Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks IEEE Access Smart transportation CLIP image classification |
| title | Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks |
| title_full | Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks |
| title_fullStr | Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks |
| title_full_unstemmed | Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks |
| title_short | Improved CLIP-ILP Model for Detecting Illegal Passenger Transport in Freight Trucks |
| title_sort | improved clip ilp model for detecting illegal passenger transport in freight trucks |
| topic | Smart transportation CLIP image classification |
| url | https://ieeexplore.ieee.org/document/10836735/ |
| work_keys_str_mv | AT xuanwu improvedclipilpmodelfordetectingillegalpassengertransportinfreighttrucks AT wenlinpan improvedclipilpmodelfordetectingillegalpassengertransportinfreighttrucks |