摘 要：针对常用语义分割算法存在丢失边缘信息导致分割不够精确的问题，通过结合边缘检测算法进行语义分割，有效地改善了分割不准确及边缘模糊的问题。算法采用并行结构，通过边缘检测子网络所提取的边缘特征来对语义分割子网络所提取的语义分割特征进行信息的补充，采用concat 融合两路特征进行卷积操作来获取最终分割结果。实验基于TensorFlow 平台进行，所提出方法相比以往算法在计算速度接近的同时真实值和预测值的交并比上取得了一定提升，增强了分割结果。
关键词：图像分割；边缘检测；深度学习；全卷积神经网络
中图分类号：TP391.41；TP242 文献标识码：A 文章编号：2096-4706（2020）24-0101-05
Semantic Segmentation Algorithm Based on Improved Sobel Operator
LIU Qinghua1，2，ZHONG Chen1，2，XU Jinxiu1，2，HAN Yuchen1，2
（1.School of Space Information and Surveying Engineering，Anhui University of Science and Technology，Huainan 232001，China；2.Key Laboratory of Aviation-Aerospace-Ground Cooperative Monitoring and Early Warning of Coal Mining-Induced Disasters of AnhuiHigher Education Institutes，Anhui University of Science and Technology，Huainan 232001，China）
Abstract：Aiming at the problem of inaccurate segmentation caused by the loss of edge information in common semanticsegmentation algorithms，the problem of inaccurate segmentation and fuzzy edge is effectively improved by semantic segmentationcombined with edge detection algorithm. The algorithm adopts parallel structure. The edge features extracted by the edge detection subnetworkare used to supplement the semantic segmentation features extracted by the semantic segmentation sub-network. The finalsegmentation result is obtained by convolution operation of concat fusion of two features. The experiment is based on TensorFlow platform.Compared with the previous algorithm，the proposed method achieves a certain improvement in the intersection and union ratio of the realvalue and the predicted value，and enhances the segmentation results.
Keywords：image segmentation；edge detection；deep learning；fully convolutional network
基金项目：国家自然科学基金资助项目（41474026）﹔安徽省教育厅资助项目（2018jyxm0192）
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作者简介：刘清华（1997—），男，汉族，山西晋中人，硕士研究生在读，研究方向：图像识别与图像处理。