Inshore Warship Detection Method Based on Multi-task Learning

doi:10.3969/j.issn.1006-2475.2024.03.005

Abstract

Abstract: Abstract: In the task of inshore warship detection in remote sensing optical images， this paper proposes an inshore warship detection method based on multi-task learning for the false alarms problem of similar features in complex scenes. By constructing a parallel dual-branch task framework for the sea-land segmentation mission and the warship detection mission， this method optimizes the traditional task of serial processing into parallel processing mode. Secondly， we propose a joint loss constraint for dual path optimum training， which improves the stability of model training. Finally， the dataset made by Google Earth remote sensing images is used for experiments. The detection results in land mask are eliminated by the dual-branch fusion model， and the land false alarm filter is realized. Compared with the single task detection algorithm YOLOv5， the mAP of the proposed method increased by 4.4 percentage points and the false alarm rate decreased by 3.4 percentage points. The experimental results show that the proposed algorithm is effective in suppressing false alarm on land.

Key words: Key words: warship detection, sea-land segmentation, multi-task learning, loss function

CLC Number:

TP391

LIU Xin-pin1, 2, 3, WANG Hong1, 3, ZHAO Liang-jin1, 3. Inshore Warship Detection Method Based on Multi-task Learning[J]. Computer and Modernization, 2024, 0(03): 29-33.

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