Papers › MASNet: A Robust Deep Marine Animal Segmentation Network

MASNet: A Robust Deep Marine Animal Segmentation Network

1 May 2023IEEE Journal of Oceanic Engineering 2023 5archive 2025-07-28

Zhenqi Fu, Ruizhe Chen, Yue Huang, En Cheng, Xinghao Ding, Kai-Kuang Ma

Marine animal studies are of great importance to human beings and instrumental to many research areas. How to identify such animals through image processing is a challenging task that leads to marine animal segmentation (MAS). Although deep neural networks have been widely applied for object segmentation, few of them consider the complex imaging condition in the water and the camouflage property of marine animals. To this end, a robust deep marine animal segmentation network is proposed in this article. Specifically, we design a new data augmentation strategy to randomly change the degradation and camouflage attributes of the original objects. With the augmentations, a fusion-based deep neural network constructed in a Siamese manner is trained to learn the shared semantic representations. Moreover, we construct a new large-scale real-world MAS data set for conducting extensive experiments. It consists of over 3000 images with various underwater scenes and objects. Each image is annotated with an object-level mask and assigned to a category. Extensive experimental results show that our method significantly outperforms 12 state-of-the-art methods both qualitatively and quantitatively.

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Code

zhenqifu/MASNet pytorch report

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Tasks

Data Augmentation Image Segmentation Marine Animal Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Segmentation	MAS3K	MASNet	E-measure	0.906	#3 of 4	Archive leaderboard	report
Image Segmentation	MAS3K	MASNet	MAE	0.032	#3 of 4	Archive leaderboard	report
Image Segmentation	MAS3K	MASNet	S-measure	0.864	#3 of 4	Archive leaderboard	report
Image Segmentation	MAS3K	MASNet	mIoU	0.742	#3 of 4	Archive leaderboard	report
Image Segmentation	RMAS	MASNet	E-measure	0.920	#3 of 4	Archive leaderboard	report
Image Segmentation	RMAS	MASNet	MAE	0.024	#3 of 4	Archive leaderboard	report
Image Segmentation	RMAS	MASNet	S-measure	0.862	#3 of 4	Archive leaderboard	report
Image Segmentation	RMAS	MASNet	mIoU	0.731	#3 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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