Title: Research on target recognition of UAV remote sensing image based on improved mask R-CNN model

Authors: Zufang Yang

Addresses: College of Artificial Intelligence, Wuhan Technology and Business University, Wuhan, 430056, China

Abstract: To solve the problems of low visible edge, low average gradient, low value signal-to-noise ratio, low overlap, high loss rate of image feature details and poor recognition quality existing in traditional methods, a target recognition method of UAV remote sensing image based on improved mask R-CNN model is proposed. Firstly, multi-scale Retinex and dark channel are used to process fog in UAV remote sensing images. Secondly, the mask R-CNN model is improved through pyramid balance strategy and bypass connection. Finally, the demobilised image is input into the improved model to realise remote sensing image target recognition. The experimental results show that the highest values of visible edge and average gradient of the method reach 0.988 and 992, the highest value of PSNR reaches 67.47, the highest value of overlap reaches 98.7%, and the loss rate of detail is below 1.2%, the recognition quality is high.

Keywords: improved mask R-CNN model; UAV; remote sensing images; target recognition; restore the image; feature extraction.

DOI: 10.1504/IJCISTUDIES.2023.132488

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.1/2, pp.72 - 91

Received: 11 Oct 2022
Accepted: 01 Dec 2022
Published online: 24 Jul 2023 *

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