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面向低光照环境的车辆目标检测方法
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Vehicle Object Detection Method for Low-light Environment
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    摘要:

    在智慧交通系统和城市安全领域中,准确获取车辆信息至关重要. 通过视频或图像等视觉识别手段可以直接获取车辆相关信息. 然而,在低光照环境下,图像亮度和对比度降低、噪声增加、图像细节特征易丢失,这些问题导致车辆目标检测算法的精度大大降低. 为此,提出了一种基于低光照图像增强算法和改进目标检测算法的车辆检测方法. 首先,利用图像增强算法ZeroDCE对低光照图像进行增强,以提升图像亮度; 然后,利用改进的AFF-YOLO目标检测网络对增强后的图像进行车辆检测;最后,将本文方法在车辆数据集上进行测试,并分析不同低光照等级对于车辆检测精度的影响. 结果表明,本文方法能够有效提升车辆目标检测的精度,与低光照图像相比,增强后图像的目标检测精度mAP@0.5提升了4.9%,达到94.7%;而且光照强度越低,增强后图像的目标检测精度提升越显著. 研究成果可为低照度环境下的车辆检测提供参考.

    Abstract:

    In the field of intelligent transportation systems and urban security, it is crucial to obtain accurate information of vehicles. Vehicle-related information can be directly obtained through visual recognition means such as video or images. However, in low-light environments, the image brightness and contrast decrease, the noise level increases, and the image features are prone to loss. These problems lead to a significant reduction in the detection accuracy of vehicle detection algorithms. Therefore, we propose a vehicle detection method based on low-light image enhancement and an improved object detection algorithm. The low-light image was first enhanced using the image enhancement algorithm ZeroDCE to improve the image brightness. Then, the improved AFF-YOLO object detection algorithm is utilized to perform vehicle detection on the enhanced image. Finally, the proposed method is tested on a vehicle dataset, and the vehicle detection accuracy under different low-light levels is analyzed. The results show that the proposed method can effectively improve the vehicle detection accuracy. Compared with low-light images, mAP@0.5 of the enhanced images improved by 4.9% to 94.7%. As the illumination intensity decreases, the object detection accuracy of the enhanced image improves more significantly. The research results can provide a reference for vehicle detection in low-light environments.

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孔烜 ?,彭佳强 ,张杰 ,戴剑军 ,潘思宇 ,吴政奇 .面向低光照环境的车辆目标检测方法[J].湖南大学学报:自然科学版,2025,52(1):187~195

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  • 在线发布日期: 2025-01-22
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