The Segmentation Method of Road Surface Covering Objects Based on CBAM UNET++

Abstract

Dangerous road surface covering objects such as wet slippery, ice and snow will directly affect the safety performance. Therefore, the detection and visualization of road surface covering objects' status under complex weather and road conditions are of great significance to the safety of human driving and unmanned driving. However, the complex road conditions (vehicles and pedestrians blocking the road surface, the area of the measured coverage is small, and the ambient light changes drastically) limit the accuracy of road surface coverage objects' state detection in the natural environment. Given the above problems, this paper reconstructs the image prepossessing process in road ice and snow cover segmentation by introducing background extraction before image segmentation, and then proposes a road surface coverage objects segmentation method based on Convolutional Block Attention Module UNet++ (CBAM UNet++). First, through the performance comparison of different background extraction algorithms, the Content-adaptive Resizing Framework (CARF) background extraction algorithm is used to eliminate the interference of vehicles, pedestrians and other objects in complex road conditions. Then, the CBAM UNet++ model is established to segment the four types of road surface coverings objects in the outfield to improve detection accuracy under conditions of small area coverage objects and severe illumination changes. Experimental results indicate, after introducing background extraction, the segmentation accuracy under different lighting conditions can be improved by 5.6%--17.7%; Compared with traditional methods for segmenting objects on road surfaces, the CBAM UNet++ method demonstrates an average segmentation accuracy improvement of at least 6.5% under six different lighting conditions.