多特征融合的道路场景语义分割算法

为提升道路场景语义分割的性能以及实际应用性，本文将传统的图像处理算法与深度学习技术相结合，提出了一种多特征融合的轻量级道路场景语义分割网络模型。该模型首先利用颜色空间转化、图像均衡化、边缘检测等算法来对图像多种特征信息进行增强；其次，以深度可分离卷积为基本单元搭建高效率特征提取结构，对特征增强后的图像进行信息融合和提取，并结合跳层上采样操作完成初步分割；最后，引入边缘检测支路来对分割图像的目标边界信息进行细化，保障网络高精度分割。通过实验结果表明，所提网络在分割精度、计算效率上得到了较好的平衡，同时，在实际变电站道路场景应用中，该网络也能实现高效语义分割，为巡检机器人提供有效的道路信息。

Research on Road Scene Semantic Segmentation Algorithm Based on Multi Feature Fusion

In order to improve the performance and practical application of road scene semantic segmentation, A lightweight multi feature fusion network combining traditional image processing and deep learning technology is proposed for road scene semantic segmentation. Firstly, algorithms such as color space transformation, image equalization and edge detection are used to enhance image feature information. Secondly, the depth separable convolution is used as the basic unit to build a high-efficiency feature extraction structure, which is used for information fusion and extraction of the image after feature enhancement, and the preliminary segmentation is realized combined with the skip layer upsampling operation; Finally, the edge detection branch is introduced to refine the boundary information of the segmented image to ensure the high-precision segmentation of the network. Experimental results show that the proposed network effectively balances the network segmentation accuracy and computational efficiency. At the same time, in the actual application of substation road scene, the network can also achieve efficient semantic segmentation and provide effective road information for inspection robots.