Edge-guided Representation Learning for Underwater Object Detection
Linhui Dai Hong Liu Pinhao Song Hao Tang Runwei Ding Shengquan Li
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. We 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, we
propose an Edge-guided Representation Learning Network, termed ERL-Net, that
aims to achieve discriminative representation learning and aggregation under
the guidance of edge cues. Firstly, we introduce an edge-guided attention
module to model the explicit boundary information, which generates more
discriminative features. Secondly, a 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
recognizing underwater objects. Finally, we propose a wide and asymmetric
receptive field block to enable features to have a wider receptive field,
allowing the model to focus on more small object information. Comprehensive
experiments on three challenging underwater datasets show that our method
achieves superior performance on the UOD task.