Research On Image Semantic Segmentation Algorithm For Autonomous Driving Scenario

Image semantic segmentation is the process of assigning each pixel in an image to its corresponding category,enabling the recognition and segmentation of different objects within the image.This technique plays a crucial role in autonomous driving systems by providing vehicles with essential environmental perception information such as obstacles,drivable areas,and traffic signs.However,image semantic segmentation in autonomous driving scenarios faces two significant challenges: accuracy and real-time performance.Road traffic environments are complex and diverse,with large target differences and visual sensors easily affected by lighting conditions,resulting in low imaging quality under harsh conditions.Additionally,vehicles travel at high speeds with vast amounts of environmental perception information,making it challenging for image segmentation to meet real-time requirements.To address these challenges,this paper presents research on image semantic segmentation algorithms for autonomous driving scenarios.The specific contributions are as follows:(1)We improve a high-accuracy image semantic segmentation scheme that leverages HRNet(High-Resolution Net)to maintain high-resolution features while incorporating OCRNet(Object-Contextual Representations for Semantic Segmentation)to enhance object-level semantic information.To further improve segmentation performance,we introduce three improvements to the original method: an improved upsampling module that assigns different weights according to the importance of channels in the network model for more targeted upsampling;an optimized network structure that increases the connection between shallow and deep features;and an improved HRNet convolution module that uses residual ASPP(Atrous Spatial Pyramid Pooling)instead of ordinary convolution to enhance multi-scale perception capabilities.Experimental results demonstrate that our proposed scheme achieves mean Intersection over Union(m Io U)scores of 81.20% and72.06% on the Cityscapes and Camvid(Cambridge-driving Labeled Video Database)datasets,respectively.(2)We improve a real-time image semantic segmentation scheme based on knowledge distillation and feature fusion.Our approach includes four main components: using a large model with many parameters(teacher network)to supervise the training of a lightweight small model(student network),enabling the student network to learn effective semantic information from the teacher network;an improved flexible decoder module that corresponds its network channel number to the input image size to avoid computational redundancy;an improved attention-based feature fusion module that optimizes feature interaction and fusion;and an optimized pyramid pooling module structure that reduces computational overhead and memory consumption.Experimental results show that our proposed scheme effectively improves the segmentation speed of semantic segmentation algorithms on the Cityscapes dataset,further enhancing the practicality and application scope of semantic segmentation technology.(3)We design a semantic segmentation system for driving scenarios.The system is based on semantic segmentation techniques to achieve pixel-level classification of various targets in vehicle scenes.It provides a visual interface to display the segmentation results,and also serves as a general research platform that supports comparison and evaluation of different semantic segmentation algorithms.The system consists of four layers: data acquisition layer,data storage layer,business layer and presentation layer.After testing and verification,we found that the system can perform pixel-level semantic annotation of driving scene images,support switching and performance comparison of multiple semantic segmentation models,and also visualize the segmentation results.In summary,this paper studies and experiments on the accuracy and real-time requirements of semantic segmentation techniques for autonomous driving and proposes a high-accuracy image semantic segmentation scheme and a real-time image semantic segmentation scheme,which improve the overall performance of semantic segmentation techniques for autonomous driving.Moreover,a driving scene semantic segmentation system is designed,which provides a visual analysis platform for the semantic segmentation problem of autonomous driving.