Liu Zhaohui, Zhang Huiru, Lin Lifei
College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel). 2025 Jan 1;25(1):194. doi: 10.3390/s25010194.
To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments. Secondly, the YOLOX network is optimized by adding attention mechanisms and an image enhancement module to improve feature extraction and training. Additionally, by combining this with the characteristics of foggy environment images, the loss function is optimized to further improve the target detection performance of the network in foggy environments. Finally, transfer learning is applied during the training process, which not only accelerates network convergence and shortens the training time but also further improves the robustness of the network in different environments. Compared with YOLOv5, YOLOv7, and Faster R-CNN networks, the mAP of the improved network increased by 13.57%, 10.3%, and 9.74%, respectively. The results of the comparative experiments from different aspects illustrated that the proposed method significantly enhances the detection performance for vehicle targets in foggy environments.
为解决车载视觉传感器在雾天环境下目标检测存在的问题，提出一种基于改进YOLOX网络的车辆目标检测方法。首先，为解决雾天交通场景图像中车辆目标特征丢失的问题，将雾影响图像的特定特征融入网络训练过程。这不仅增加了训练数据，还提高了网络在雾天环境下的鲁棒性。其次，通过添加注意力机制和图像增强模块对YOLOX网络进行优化，以改进特征提取和训练。此外，结合雾天环境图像的特点，对损失函数进行优化，进一步提高网络在雾天环境下的目标检测性能。最后，在训练过程中应用迁移学习，这不仅加速了网络收敛，缩短了训练时间，还进一步提高了网络在不同环境下的鲁棒性。与YOLOv5、YOLOv7和Faster R-CNN网络相比，改进网络的平均精度均值（mAP）分别提高了13.57%、10.3%和9.74%。不同方面的对比实验结果表明，所提方法显著提高了雾天环境下车辆目标的检测性能。
