Road Surface Segmentation and Detection Under Extreme Weather Conditions Based on Mask-RCNN

The recognition effect of road surface conditions is a crucial factor that impacts traffic safety management and control. However, conventional intelligent models face challenges in accurately detecting road surfaces during extreme weather, which significantly hampers the recognition effect. This paper proposes an intelligent detection model for detecting road surfaces under extreme weather conditions. The model uses a Mask-RCNN architecture with Resnet101 and Feature Pyramid Network layers as its backbone network. In addition, we present a detailed study of the parameter training method for the model. This enables the design of a complete process for intelligent road surface detection in severe weather. The experimental results demonstrate that the proposed model scan accurately detect road surfaces, ultimately improving the recognition accuracy of road surface conditions. The processing method eliminates interference in the image and subsequently utilizes the proposed model to conduct object detection and segmentation on the processed image.