Satellite target detection algorithm for high-pressure dense channel based on deep learning

Article type: Research Article

Authors: Wang, Yajun^{; *}

Affiliations: Taizhou Vocational College of Science & Technology, Taizhou, Zhejiang, China

Correspondence: [*] Corresponding author. Yajun Wang, Taizhou Vocational College of Science & Technology, Taizhou, Zhejiang, 318020, China. E-mail: [email protected].

Abstract: In order to improve the detection accuracy of high-voltage dense channel satellite image, a satellite target detection algorithm based on deep learning is proposed. The convolution neural network is selected to extract the feature map of high-voltage dense channel satellite image, and the extracted feature map is input into the optimized deformation convolution neural network. The value of each sampling point and the corresponding position authority of block convolution kernel are weighted by using the regular region sampling feature map. The feature map output by the convolution operation of pooling layer is used to obtain the depth features of the same dimension. The depth feature is input into the full connection layer to obtain the full connection feature of candidate target area, and the target detection in high-voltage dense channel satellite image is realized. The experimental results show that the target detection accuracy of the method is higher than 99% and the false alarm rate and false alarm rate are lower than 1.4%.

Keywords: Deep learning, high voltage dense channel, satellite, target detection algorithm, convolution neural network, regular region

DOI: 10.3233/JIFS-223936

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5861-5869, 2023