An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

• 作者: Haibin Yu Shuaishuai Lv Xiao Jiang
• 来源期刊: 通讯、网络与系统学国际期刊(英文)
• 学科: 工学
• 关键词: Image Segmentation Local Region Condition Random Field Model Deep Neural Network Consecutive Shooting Traffic Scene
• 年，卷（期）: 2020,（9）
• 页码范围: 139-159
• 页数: 21页
• 分类号: TP3

摘要

To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.