Lightweight Vehicle Detection Algorithm Based on RT-DETR in Foggy Weather Scenarios
1. School of Computer Science, Sichuan Normal University, Chengdu 610101, China
2. School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu 610101, China
| Abstract: | Addressing the difficulty in balancing detection accuracy and model lightweighting in vehicle detection tasks under foggy conditions, this paper proposes an improved foggy vehicle detection algorithm based on RT-DETR-r18. This algorithm overcomes the computational bottleneck of the model. First, an improved backbone network (C2F_DYNCB) module is designed, combining a dynamic convolution weight (DKW) mechanism and parameter sharing (DyInConv) to construct a novel dynamic convolution unit (DAIN Mixer). This module enables adaptive depth convolution and feature fusion, significantly reducing the number of parameters and computations while still maintaining detailed processing of input feature maps. Second, a PEMD module is proposed, introducing the linear attention mechanism Pola Attention and EDFFN, which significantly improves the discriminative power of the attention map and model performance while maintaining linear complexity. Then, a lightweight adaptation block, the MOEN module, is designed to further integrate local information and stabilize feature distribution. Finally, WIoU-v3 is used as the regression loss, adaptively adjusting the weights of positive and negative samples during training to increase attention to subtle bounding boxes. Experimental results show that on the REFYG foggy dataset, the improved algorithm's mAP50 is 3.05 percentage points higher than the original algorithm, and the model's computational complexity and number of parameters are reduced by 39.4% and 32.8%, respectively, achieving significant lightweighting. |
| Keywords: | RT-DETR; Foggy Weather Detection; Lightweight Model; Attention Mechanism; Dynamic Convolution |
| DOI: | 10.57237/j.se.2026.01.001 |
View:
Downloads:
Received: 11 December 2025
Accepted: 13 January 2026
Published Online: 3 February 2026
Funding
| 1. | 国家社会科学基金一般项目 (20BMZ092) |
References
| [1] | Girshick R, Donahue J, Darrell T, et al. Region-based convolutional networks for accurate object detection and segmentation [J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 38(1): 142-158. |
| [2] | Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [J]. Advances in neural information processing systems, 2017, 30. |
| [3] | Zhao Y, Lv W, Xu S, et al. Detrs beat yolos on real-time object detection [C] // Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024: 16965-16974. |
| [4] | Chen Y, Dai X, Liu M, et al. Dynamic convolution: Attention over convolution kernels [C] // Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 11030-11039. |
| [5] | Meng W, Luo Y, Li X, et al. PolaFormer: Polarity-aware linear attention for vision transformers [J]. arXiv preprint arXiv: 2501.15061, 2025. |
| [6] | Ciubotariu G, Vasluianu F A, Zhou Z, et al. AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results [C] // Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025: 5541-5550. |
| [7] | Zhu J, Chen X, He K, et al. Transformers without normalization [C] // Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 14901-14911. |
| [8] | Tong Z, Chen Y, Xu Z, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism [J]. arXiv preprint arXiv: 2301.10051, 2023. |
| [9] | Cheng L, Zhang D, Zheng Y. Road object detection in foggy complex scenes based on improved YOLOv8 [J]. IEEE Access, 2024. |
| [10] | 孟志永,吴晨曦,王鹏,等.基于改进RT-DETR的井盖病害轻量化检测算法 [J/OL]. 计算机工程与应用, 1-13. (2025-09-28). https://doi.org/10.3778/j.issn.1002-8331.2506-0079 |
| [11] | Chen Y, Wang Y, Zou Z, et al. GMS-YOLO: A Lightweight Real-Time Object Detection Algorithm for Pedestrians and Vehicles Under Foggy Conditions [J]. IEEE Internet of Things Journal, 2025. |
| [12] | Li B, Ren W, Fu D, et al. Benchmarking single-image dehazing and beyond [J]. IEEE transactions on image processing, 2018, 28(1): 492-505. |
| [13] | Sakaridis C, Dai D, Van Gool L. Semantic foggy scene understanding with synthetic data [J]. International Journal of Computer Vision, 2018, 126(9): 973-992. |
| [14] | Kenk M A, Hassaballah M. DAWN: vehicle detection in adverse weather nature dataset [J]. arXiv preprint arXiv: 2008.05402, 2020. |
| [15] | Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector [C] // European conference on computer vision. Cham: Springer International Publishing, 2016: 21-37. |
| [16] | Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks [J]. Advances in neural information processing systems, 2015, 28. |
| [17] | Reis D, Kupec J, Hong J, et al. Real-time flying object detection with YOLOv8 [J]. arXiv preprint arXiv: 2305.09972, 2023. |
| [18] | Wang A, Chen H, Liu L, et al. Yolov10: Real-time end-to-end object detection [J]. Advances in Neural Information Processing Systems, 2024, 37: 107984-108011. |
| [19] | Khanam R, Hussain M. Yolov11: An overview of the key architectural enhancements [J]. arXiv preprint arXiv: 2410.17725, 2024. |
| [20] | Tian Y, Ye Q, Doermann D. Yolov12: Attention-centric real-time object detectors [J]. arXiv preprint arXiv: 2502.12524, 2025. |
| [21] | Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers [C] // European conference on computer vision. Cham: Springer International Publishing, 2020: 213-229. |
Join the Editorial Board
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsBecome a Reviewer
Scholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us