Edge-guided representation learning for underwater object detection

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Linhui Dai, Hong Liu, Pinhao Song, Hao Tang, Runwei Ding, Shengquan Li
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引用次数: 0

Abstract

Underwater object detection (UOD) is crucial for marine economic development, environmental protection, and the planet's sustainable development. The main challenges of this task arise from low-contrast, small objects, and mimicry of aquatic organisms. The key to addressing these challenges is to focus the model on obtaining more discriminative information. The authors observe that the edges of underwater objects are highly unique and can be distinguished from low-contrast or mimicry environments based on their edges. Motivated by this observation, an Edge-guided Representation Learning Network, termed ERL-Net is proposed, that aims to achieve discriminative representation learning and aggregation under the guidance of edge cues. Firstly, an edge-guided attention module is introduced to model the explicit boundary information, which generates more discriminative features. Secondly, a hierarchical feature aggregation module is proposed to aggregate the multi-scale discriminative features by regrouping them into three levels, effectively aggregating global and local information for locating and recognising underwater objects. Finally, a wide and asymmetric receptive field block is proposed to enable features to have a wider receptive field, allowing the model to focus on smaller object information. Comprehensive experiments on three challenging underwater datasets show that our method achieves superior performance on the UOD task.

Abstract Image

用于水下物体探测的边缘引导表示学习
水下物体探测(UOD)对于海洋经济发展、环境保护和地球的可持续发展至关重要。这项任务的主要挑战来自低对比度、小物体和水生生物的模仿。应对这些挑战的关键在于将模型的重点放在获取更具辨别力的信息上。作者观察到,水下物体的边缘非常独特,可以根据其边缘从低对比度或模仿环境中区分出来。基于这一观察结果,作者提出了边缘引导表征学习网络(ERL-Net),旨在边缘线索的引导下实现分辨表征学习和聚合。首先,引入边缘引导注意力模块来模拟明确的边界信息,从而生成更具区分性的特征。其次,提出了一个分层特征聚合模块,通过将多尺度判别特征重新组合为三个层次来聚合这些特征,从而有效地聚合全局和局部信息,用于定位和识别水下物体。最后,还提出了一个宽而不对称的感受野块,使特征具有更宽的感受野,从而使模型能够专注于更小的物体信息。在三个具有挑战性的水下数据集上进行的综合实验表明,我们的方法在 UOD 任务中取得了优异的性能。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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