基于多级特征提取的低光照目标检测算法

Low-Light Target Detection Algorithm Based on Multi-Level Feature Extraction

自然环境下普遍存在着低光照场景,导致目标检测任务精度降低。提出了一种低光照目标检测算法MLFEYOLOX。引入轻量级图像增强算法IAT,还原低照度图像更多的细节。设计了一种多级特征提取的CSP-M模块,加强模型在低光照条件下图像的特征提取能力。引入卷积注意力机制CBAM,自适应测量目标位置与背景信息的相关程度,降低背景信息带来的干扰。设计了多级特征融合模块CSP-MC,增强模型融合多级特征和发掘并融合静态和动态上下文信息的能力。采用ExDark、UFDD数据集进行实验验证,实验结果表明,该方法有效克服了光照不足带来的影响,与主流算法相比检测精度明显提升。

The low-light scene is prevalent in natural environments which can reduce target detection accuracy.A low-light object detection algorithm MLFE-YOLOX is proposed.Firstly,the lightweight image enhancement algorithm IAT is introduced to restore more details of low-illumination images.Secondly,a CSP-M module for multi-level feature extrac-tion is designed to strengthen the performance of the feature extraction model under low light conditions.Then,the convo-lutional attention mechanism CBAM is introduced to adaptively measure the correlation between target position and back-ground information,which reduces the interference caused by background information.Finally,a multi-level feature fusion module CSP-MC is designed to enhance the model’s ability to fuse multi-level features and the ability to explore and fuse static and dynamic contextual information.ExDark and UFDD datasets are used for experimental verification,and the experimental results show that the proposed method effectively overcomes the influence caused by under illumination,and the detection accuracy is significantly improved in comparison with the mainstream algorithms.