Fourier contour embedding deep learning for arbitrary-shaped target detection

Xueying Wang; Feng Zhang; Yuan Huang

doi:10.1117/12.2668014

21 March 2023 Fourier contour embedding deep learning for arbitrary-shaped target detection

Xueying Wang, Feng Zhang, Yuan Huang

Proceedings Volume 12595, Advanced Fiber Laser Conference (AFL2022); 1259517 (2023) https://doi.org/10.1117/12.2668014
Event: Advanced Fiber Laser Conference (AFL2022), 2022, Changsha, China

Abstract

Target detection of arbitrary shape is widely used in remote sensing image processing, the case segmentation method based on contour regression is very similar to the target detection method. At present, many scholars have studied case segmentation using deep learning framework, case segmentation based on contour regression can be regarded as a simple extension of object detection, it is a more accurate target detection. In this paper, the contour regression method based on Fourier operator is used for accurate target detection, the Fourier feature vectors in a group of frequencies are predicted at each position of the feature map, and then the target contour point sequence is reconstructed in the image space domain through inverse Fourier transform, The simulation results show that the detection accuracy of the proposed method is more than 10% higher than that of the classical method.

Citation Download Citation

Xueying Wang, Feng Zhang, and Yuan Huang "Fourier contour embedding deep learning for arbitrary-shaped target detection", Proc. SPIE 12595, Advanced Fiber Laser Conference (AFL2022), 1259517 (21 March 2023); https://doi.org/10.1117/12.2668014

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