基于偏振图像的低照度场景多目标检测算法

偏振光反射信息可直接反演目标本征特性，且在传输过程中具备较强的抗干扰特性，因此偏振成像技术可适用于多种复杂环境中的智能监控、交通监察领域。近年来使用深度学习判读图像检测目标的方法迅速发展，已经广泛应用于图像处理的各个领域。本文提出了一种基于偏振图像与深度神经网络算法的行人、车辆多目标检测算法YOLOv5s-DOLP。首先，通过实时获取到偏振图像进行偏振信息解析，获取目标偏振度图像。其次，为增强偏振度图像中检测目标与背景存在高对比度的特性，在主干网络中引入通道注意力与空间注意力，提升网络特征进行自适应学习的能力。此外，使用K-means算法对目标位置信息进行聚类分析，加快网络在偏振度图像的学习速度，提升目标检测精度。实验结果显示，该算法结合了偏振成像和深度学习目标检测的优势，对于低照度复杂场景中的车辆、行人目标检测效果好、检测速度快，对于道路车辆的目标检测、识别与跟踪具有一定的应用价值。

Multi-Target Detection of Low-Illuminance Scene Based on Polarization Image

Polarized light reflection information can directly invert the intrinsic characteristics of a target and has strong anti-interference characteristics in the transmission process. Thus, polarization imaging technology can be applied to the fields of intelligent monitoring and traffic monitoring in various complex environments. In recent years, deep-neural-network methods for interpreting image detection targets have been developed rapidly and widely used in various fields of image processing. In this study, a vehicle multi-target detection algorithm based on polarized images and deep learning is proposed. First, the target polarization degree image can be obtained by acquiring the polarization image in real time and analyzing the polarization information. Second, to enhance the high contrast between the detection targets and the background in the polarization image, channel attention and spatial attention are introduced into the backbone network to improve the ability of the network features to perform adaptive learning. In addition, the K-means algorithm is used to perform clustering analysis on the target location information, thereby increasing the network's learning speed in the polarization image and improving the progress of target detection. The experimental results show that this method is effective and fast for vehicle detection in complex scenes with low illumination. This method combines the advantages of polarization imaging and deep-learning target detection and has substantial application scope in road vehicle target detection, recognition, and tracking.