基于图像特征增强的低照度目标检测算法

Low-light object detection algorithm based on image feature enhancement

低照度环境会导致图像目标特征不明显,噪声干扰严重等情况,影响目标检测器的检测性能。针对以上问题,构建了一个多尺度图像特征增强模块FEM,并与YOLOv8s目标检测网络联合,构建了端到端的低照度目标检测算法FE-YOLO。首先,使用FEM从输入图像中构建三个不同尺度下的特征信息并进行高效融合,得到具有丰富特征表达的增强图像。然后,在YOLOv8s颈部网络中添加目标特征增强模块TFE,通过抑制高层特征中的背景噪声信息,突出目标特征的表达能力。实验结果表明:在低照度图像目标检测数据集ExDark上的平均精度均值(mAP)达到了75.63%,与原始的YOLOv8s算法相比,提高了3.03%,本文算法在低照度目标检测任务中取得了更好的检测效果。

Low illumination environments can lead to situations such as inconspicuous image target features and severe noise interference,which affect the detection performance of the object detector.To address the above problems,a multi-scale image feature enhancement module FEM is constructed,and in conjunction with YOLOv8s object detection network,an end-to-end low-light image object detection method FE-YOLO is constructed.Firstly,FEM is employed to extract feature information from the input image at three different scales and efficiently fuse them to obtain an enhanced image with rich feature representation.Then,in the neck network of YOLOv8s,a target feature enhancement module TFE is incorporated.TFE works by suppressing background noise information in higher-level features,thereby accentuating the representation capacity of target features.The experimental results show that the mean average precision mean(mAP)on the low-light image object detection dataset ExDark reaches 75.63%,which is 3.03%higher than the original YOLOv8s algorithm,and this paper′s algorithm achieves a better detection result in the low-light object detection task.