Proceedings ArticleDOI
Adversarial unsupervised domain adaptive inland vessel detection method
Liang Gao,Gao Shu +1 more
- Vol. 12635, pp 126351G-126351G
TLDR
In this paper , an adversarial unsupervised domain adaptive method is proposed, using labeled sunny samples and unlabeled foggy samples, which can adaptively detect ship targets in foggy conditions.Abstract:
Aiming at the problems that the target detection accuracy of inland watercraft is reduced, and the difficulty of labeling samples is increased due to fog, according to the characteristics of inland water ship detection, an adversarial unsupervised domain adaptive method is proposed, using labeled sunny samples and unlabeled foggy samples, which can adaptively detect ship targets in foggy conditions. On the basis of Faster-RCNN, a ship local feature domain discriminator based on full convolution and a ship global feature domain discriminator based on a hybrid attention mechanism are respectively constructed to realize the features of sunny and foggy ship images in multi-level domain space alignment, to better complete the adaptive detection from sunny ship data to foggy ship data. At the same time, using the idea of adversarial learning, a local feature adversarial loss function based on the least square method and a global feature adversarial loss based on cross entropy are designed to strengthen the adversarial training between feature extractor and domain discriminator. The experimental results on the inland ship data set show that only using the marked sunny ship data and the unmarked foggy ship data can adaptively detect ships in foggy weather, and the accuracy and real-time performance are significantly higher than those of the comparison model.read more
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