Cross-Level Semantic Segmentation Guided Feature Space Decoupling And Augmentation for Fine-Grained Ship Detection

Zhang, Zhengning; Zhang, Lin; Wang, Yue; Feng, Pengming; Liu, Shaobo; Wang, Jian

doi:10.23919/eusipco55093.2022.9909586

Cited by 2 publications

(1 citation statement)

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“…Therefore, using quantity as the definition of majority and minority classes is unreasonable in remote sensing images. Zhang et al [3] proposed using the average instance area to measure the class imbalance problem. Inspired by this, we adopt a weighted combination of the instance count ratio and the average pixel ratio per instance as an evaluation metric, which can better conform to the long-tail distribution characteristics of ship targets in HRORSI.…”

Section: Introductionmentioning

confidence: 99%

A data augmentation method for fine-grained ship detection in remote sensing images

Yang,

Zhang,

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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The advancement of deep learning has given significant improvement in fine-grained ship detection. However, a major challenge in deep learning based fine-grained ship detection in remote sensing images is the long-tail distribution, not only in the aspect of the number of objects but also in the average instance area of ships. Thus, models tend to be biased towards the categories with more samples or a larger average instance area, i.e., majority classes, and results in low accuracy in recognizing the categories with fewer samples or smaller average instance area, i.e., minority classes. To address this challenge, a novel data augmentation method is proposed in this paper, namely SegRotatePaste. Firstly, a weighted combination-based strategy is proposed by employing the number ratio and the average instance area ratio of ships as evaluation metrics, to identify the minority class and determine the required number of augmentations. Then sea-land segmentation and ship annotations are introduced as auxiliary semantic information to guide the embedding location of ship clips. In addition, various image embedding techniques are leveraged to achieve data augmentation for the minority class. The proposed data augmentation method is evaluated on the HRSC2016 and FGSD2021 datasets. Our method significantly improves the 𝒎𝑨𝑷 of the minority class by 9.3% and 11.5% over the baseline on the HRSC2016 and FGSD2021 datasets, respectively.

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Section: Introductionmentioning

confidence: 99%