TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions
Accurate vehicle type recognition in low-light environments remains a critical challenge for intelligent transportation systems (ITSs). To address the performance degradation caused by insufficient lighting, complex backgrounds, and light interference, this paper proposes a Twin-Stream Feature Fusio...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3613 |
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| author | Huizhi Xu Wenting Tan Yamei Li Yue Tian |
| author_facet | Huizhi Xu Wenting Tan Yamei Li Yue Tian |
| author_sort | Huizhi Xu |
| collection | DOAJ |
| description | Accurate vehicle type recognition in low-light environments remains a critical challenge for intelligent transportation systems (ITSs). To address the performance degradation caused by insufficient lighting, complex backgrounds, and light interference, this paper proposes a Twin-Stream Feature Fusion Graph Neural Network (TFF-Net) model. The model employs multi-scale convolutional operations combined with an Efficient Channel Attention (ECA) module to extract discriminative local features, while independent convolutional layers capture hierarchical global representations. These features are mapped as nodes to construct fully connected graph structures. Hybrid graph neural networks (GNNs) process the graph structures and model spatial dependencies and semantic associations. TFF-Net enhances the representation of features by fusing local details and global context information from the output of GNNs. To further improve its robustness, we propose an Adaptive Weighted Fusion-Bagging (AWF-Bagging) algorithm, which dynamically assigns weights to base classifiers based on their F1 scores. TFF-Net also includes dynamic feature weighting and label smoothing techniques for solving the category imbalance problem. Finally, the proposed TFF-Net is integrated into YOLOv11n (a lightweight real-time object detector) with an improved adaptive loss function. For experimental validation in low-light scenarios, we constructed the low-light vehicle dataset VDD-Light based on the public dataset UA-DETRAC. Experimental results demonstrate that our model achieves 2.6% and 2.2% improvements in mAP50 and mAP50-95 metrics over the baseline model. Compared to mainstream models and methods, the proposed model shows excellent performance and practical deployment potential. |
| format | Article |
| id | doaj-art-935ad00a73464d408665724fca1a5df0 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-935ad00a73464d408665724fca1a5df02025-08-20T03:16:39ZengMDPI AGSensors1424-82202025-06-012512361310.3390/s25123613TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light ConditionsHuizhi Xu0Wenting Tan1Yamei Li2Yue Tian3School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, ChinaSchool of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, ChinaSchool of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, ChinaSchool of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, ChinaAccurate vehicle type recognition in low-light environments remains a critical challenge for intelligent transportation systems (ITSs). To address the performance degradation caused by insufficient lighting, complex backgrounds, and light interference, this paper proposes a Twin-Stream Feature Fusion Graph Neural Network (TFF-Net) model. The model employs multi-scale convolutional operations combined with an Efficient Channel Attention (ECA) module to extract discriminative local features, while independent convolutional layers capture hierarchical global representations. These features are mapped as nodes to construct fully connected graph structures. Hybrid graph neural networks (GNNs) process the graph structures and model spatial dependencies and semantic associations. TFF-Net enhances the representation of features by fusing local details and global context information from the output of GNNs. To further improve its robustness, we propose an Adaptive Weighted Fusion-Bagging (AWF-Bagging) algorithm, which dynamically assigns weights to base classifiers based on their F1 scores. TFF-Net also includes dynamic feature weighting and label smoothing techniques for solving the category imbalance problem. Finally, the proposed TFF-Net is integrated into YOLOv11n (a lightweight real-time object detector) with an improved adaptive loss function. For experimental validation in low-light scenarios, we constructed the low-light vehicle dataset VDD-Light based on the public dataset UA-DETRAC. Experimental results demonstrate that our model achieves 2.6% and 2.2% improvements in mAP50 and mAP50-95 metrics over the baseline model. Compared to mainstream models and methods, the proposed model shows excellent performance and practical deployment potential.https://www.mdpi.com/1424-8220/25/12/3613vehicle type recognitionlow-light conditiongraph attention networkYOLO |
| spellingShingle | Huizhi Xu Wenting Tan Yamei Li Yue Tian TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions Sensors vehicle type recognition low-light condition graph attention network YOLO |
| title | TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions |
| title_full | TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions |
| title_fullStr | TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions |
| title_full_unstemmed | TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions |
| title_short | TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions |
| title_sort | tff net a feature fusion graph neural network based vehicle type recognition approach for low light conditions |
| topic | vehicle type recognition low-light condition graph attention network YOLO |
| url | https://www.mdpi.com/1424-8220/25/12/3613 |
| work_keys_str_mv | AT huizhixu tffnetafeaturefusiongraphneuralnetworkbasedvehicletyperecognitionapproachforlowlightconditions AT wentingtan tffnetafeaturefusiongraphneuralnetworkbasedvehicletyperecognitionapproachforlowlightconditions AT yameili tffnetafeaturefusiongraphneuralnetworkbasedvehicletyperecognitionapproachforlowlightconditions AT yuetian tffnetafeaturefusiongraphneuralnetworkbasedvehicletyperecognitionapproachforlowlightconditions |