一种基于Night-YOLOX的低照度目标检测方法

A Low-Illumination Object Detection Method Based on Night-YOLOX

由于在低照度场景下获取的图像具有亮度弱、对比度低、噪声多和细节丢失等特点,使用现有的检测模型对低照度图像进行目标检测会出现定位不准确和分类错误,从而导致最终的检测精度偏低.针对以上现象,本文提出了一种基于Night-YOLOX的低照度目标检测方法.该方法首先设计了一个低级特征聚集模块(Low-level Feature Gathering Module,LFGM)与主干网络合并.在低照度场景下捕获更多有效的低级特征有利于定位目标,该模块通过聚集浅层特征图中具有判别性的低级特征并送入高级特征图和深层卷积阶段中,以补偿在对低照度图像进行特征提取过程中边缘、轮廓和纹理等低级特征的缺失.然后,设计了一种注意力引导块(Attention Guidance Block,AGB)嵌入检测模型的颈部结构,从而减少低照度图像中噪声干扰的影响,引导检测模型推断出特征图中完整的对象区域范围并提取更多有用的对象特征信息,以提高目标分类的准确性.最后,在真实低照度图像数据集ExDark上进行实验,结果表明所提出的Night-YOLOX相比于其它主流的目标检测方法,在低照度场景下具有更好的检测性能.

Images captured in low-illumination environments often have many quality problems,such as weak bright⁃ness,low contrast,much noise,and detail loss.These problems will lead to inaccurate localization and object classification errors when using the existing object detection models to detect low-light images,resulting in low detection accuracy.Aim⁃ing at the above phenomena,this paper proposes a low-illumination object detection method called Night-YOLOX.First,the low-level feature gathering module(LFGM)is designed to be incorporated into the backbone.Capturing more effective lowlevel features in low-illumination scenes is beneficial to locating objects.The LFGM aggregates more discriminative low-lev⁃el features from the shallow feature maps and feeds them into the high-level feature maps and the deep convolution stages,so as to compensate for the loss of low-level edge,contour,and texture features during feature extraction in low-light images.Then,the attention guidance block(AGB)is designed to be embedded in the neck of the detection model.The AGB reduces the influence of noise interference in low-light images,guides the detection model to infer the complete object regions and ex⁃tract more useful object feature information,so as to improve the accuracy of object classification.Finally,experiments are conducted on the real low-light image dataset ExDark.The experimental results show that compared with other mainstream object detection methods,the proposed Night-YOLOX has better detection performance in low-illumination scenarios.