文献类型：会议论文

英文题名：Study on Improving Semantic Segmentation Using Gradient Information and High-Pass Filter

作者：Yuan, Ying[1,2]; Du, Yu[1,2]; Lv, Hejun[1,2]; Jiang, Anni[1,3]; Miao, Siqi[1,2]

第一作者：Yuan, Ying

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China; [3] Smart City College, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：Proceedings of 4th 2024 International Conference on Autonomous Unmanned Systems, 4th ICAUS 2024 - Volume III

会议日期：September 19, 2024 - September 21, 2024

会议地点：Shenyang, China

语种：英文

外文关键词：High pass filters - Image enhancement - Wiener filtering

摘要：In recent years, deep learning-based semantic segmentation has seen extensive application in areas like autonomous driving and remote sensing image processing. Numerous models aim to boost segmentation accuracy and efficient information extraction, typically using a convolution network structure for better model generalization. But they seldom pay attention to the scalability of image information and noise generation during information transmission. However, it is often difficult to optimize segmentation details. In our approach, we modify the convolution structure to fuse features from various scales and integrate additional image features with optimized information transfer to enhance accuracy. At the same time, our method does not rely on a unique network structure, which improves generalization. We analyze feature detail transfer in the convolution structure and propose two strategies to improve feature processing. Firstly, we identify causes for the loss of image detail and noise spatial distribution. Subsequently, we develop two universal methods to improve information transmission. One uses image gradient information to guide boundary segmentation and address segmentation challenges at complex boundaries. The second filters convolution results to suppress noise transmission through the network. ? Beijing HIWING Scientific and Technological Information Institute 2025.