Target Segmentation Algorithm Based on the Discrete Energy Functional

Wan, Zhi Ping

doi:10.2991/emcs-15.2015.32

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhi Ping Wan uses discrete technology in the research, and processes regional items and contour curves based on discrete energy function. When the similarity between target and background increases gradually, it can maintain good target extraction [6]. K Yuan,X Zhuang et al proposed a novel deep convolution neural network, which combines three innovative components, namely, multi-channel fusion module, enhanced Atrous space pyramid pool (EASPP) module and space-todepth (S2D) / depth-to-space (D2S) operation [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Fw-U-Net++: improvement of detection methods under the background of YoloV4 extended environment monitoring

Huang

Wang

Liu

2022

Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022)

View full text Add to dashboard Cite

As the problems caused by the global ecological environment become more and more serious, environmental protection has become an important issue in countries all over the world. In order to prevent natural disasters and monitor natural conditions, including monitoring areas affected by deforestation and floods, satellite image segmentation and automatic monitoring of forests and water bodies are becoming more and more important, which leads to the research work of satellite image segmentation in this paper. This paper is a continuation of the previous work of Fw-U-Net, and the model architecture proposed in the previous work still has many shortcomings, such as the design of the basic network architecture and the setting of parameters are not optimal, resulting in the performance can not be further improved, which means that there are more directions that can be more improved. In the work of this paper, we make more in-depth improvements on Fw-U-Net. Pay attention to the realization of a lot of details and the exploration of the essence. It has been verified that the segmentation verification scores of our upgraded model in forest cover area and water area performance test set are 87.48% (84.51%) and 90.7% (85.83%) respectively, compared with previous work. Increased by 3.11% (0.86%) and 2.71% (0.41%), respectively, and the loss value was reduced to a certain extent. Transfer learning has higher accuracy and reference value for forest and water satellite image segmentation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%