Zhao Xuanyi, Dou Xiaohan, Zheng Jihong, Zhang Gengpei
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
School of Urban Construction, Yangtze University, Jingzhou 434023, China.
Sensors (Basel). 2025 Aug 13;25(16):5014. doi: 10.3390/s25165014.
In complex traffic environments, image degradation due to adverse factors such as haze, low illumination, and occlusion significantly compromises the performance of object detection systems in recognizing vehicles and pedestrians. To address these challenges, this paper proposes a robust visual detection framework that integrates multi-stage image enhancement with a lightweight detection architecture. Specifically, an image preprocessing module incorporating ConvIR and CIDNet is designed to perform defogging and illumination enhancement, thereby substantially improving the perceptual quality of degraded inputs. Furthermore, a novel enhancement strategy based on the Horizontal/Vertical-Intensity color space is introduced to decouple brightness and chromaticity modeling, effectively enhancing structural details and visual consistency in low-light regions. In the detection phase, a lightweight state-space modeling network, Mamba-Driven Lightweight Detection Network with RT-DETR Decoding, is proposed for object detection in complex traffic scenes. This architecture integrates VSSBlock and XSSBlock modules to enhance detection performance, particularly for multi-scale and occluded targets. Additionally, a VisionClueMerge module is incorporated to strengthen the perception of edge structures by effectively fusing multi-scale spatial features. Experimental evaluations on traffic surveillance datasets demonstrate that the proposed method surpasses the mainstream YOLOv12s model in terms of mAP@50-90, achieving a performance gain of approximately 1.0 percentage point (from 0.759 to 0.769). While ensuring competitive detection accuracy, the model exhibits reduced parameter complexity and computational overhead, thereby demonstrating superior deployment adaptability and robustness. This framework offers a practical and effective solution for object detection in intelligent transportation systems operating under visually challenging conditions.
在复杂的交通环境中，由于雾霾、低光照和遮挡等不利因素导致的图像退化，严重影响了目标检测系统在识别车辆和行人方面的性能。为应对这些挑战，本文提出了一种强大的视觉检测框架，该框架将多阶段图像增强与轻量级检测架构相结合。具体而言，设计了一个包含ConvIR和CIDNet的图像预处理模块，用于执行去雾和光照增强，从而显著提高退化输入的感知质量。此外，引入了一种基于水平/垂直强度颜色空间的新型增强策略，以解耦亮度和色度建模，有效增强低光区域的结构细节和视觉一致性。在检测阶段，提出了一种轻量级状态空间建模网络，即带有RT-DETR解码的曼巴驱动轻量级检测网络，用于复杂交通场景中的目标检测。该架构集成了VSSBlock和XSSBlock模块以提高检测性能，特别是对于多尺度和被遮挡的目标。此外，还引入了VisionClueMerge模块，通过有效融合多尺度空间特征来增强对边缘结构的感知。在交通监控数据集上的实验评估表明，所提出的方法在mAP@50-90方面超过了主流的YOLOv12s模型，性能提升了约1.0个百分点（从0.759提高到0.769）。在确保具有竞争力的检测精度的同时，该模型的参数复杂度和计算开销降低，从而展示出卓越的部署适应性和鲁棒性。该框架为在视觉挑战性条件下运行的智能交通系统中的目标检测提供了一种实用有效的解决方案。
