Underwater Scene Segmentation by Deep Neural Network

Zhou, Yang; Wang, Jiangtao; Li, Baihua; Meng, Qinggang; Rocco, Emanuele; Saiani, Andrea

doi:10.31256/ukras19.12

Cited by 10 publications

(4 citation statements)

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“…Nevertheless, a few recent work [16], [15] explored the performance of contemporary deep CNN-based semantic segmentation models such as VGG-based encoderdecoders [30], [1], UNet [5], and SegNet [3] for underwater imagery. Although they report inspiring results, they only consider sea-grass, sand, and rock as object categories.…”

Section: A Semantic Segmentationmentioning

confidence: 99%

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Semantic Segmentation of Underwater Imagery: Dataset and Benchmark

Islam¹,

Edge²,

Xiao³

et al. 2020

Preprint

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In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images are rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. We also present a comprehensive benchmark evaluation of several state-of-the-art semantic segmentation approaches based on standard performance metrics. Additionally, we present SUIM-Net, a fully-convolutional deep residual model that balances the tradeoff between performance and computational efficiency. It offers competitive performance while ensuring fast end-to-end inference, which is essential for its use in the autonomy pipeline by visuallyguided underwater robots. In particular, we demonstrate its usability benefits for visual servoing, saliency prediction, and detailed scene understanding. With a variety of use cases, the proposed model and benchmark dataset open up promising opportunities for future research on underwater robot vision.

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Section: A Semantic Segmentationmentioning

confidence: 99%

“…Other datasets contain either binary annotations for salient foreground pixels [15] or semantic labels for very few object categories (e.g., sea-grass, rocks/sand, etc.) [16]. Therefore, the large-scale learningbased semantic segmentation methodologies for underwater imagery are not explored in depth in the literature.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Underwater Imagery: Dataset and Benchmark

Islam¹,

Edge²,

Xiao³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The mapping relationship between the underwater images and the segmented images was established by training the encoder-decoder network. Zhou et al [24] proposed a deep neural network architecture for underwater scene segmentation. The architecture extracted features by pre-training VGG-16 and learned to expand the lower resolution feature maps using the decoder.…”

Section: Introductionmentioning

confidence: 99%

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Yang

Feng

2022

Applied Sciences

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Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

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“…Then established the mapping relationship between the underwater images and the segmented images by training the encoder-decoder network. Zhou et al [19] have proposed a deep neural network architecture for underwater scene segmentation. The architecture extracted feature by pre-training VGG-16 and learned to expand the lower resolution feature maps by the decoder.…”

Section: Introductionmentioning

confidence: 99%

Dual-fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Yang

Chen

et al. 2021

Preprint

View full text Add to dashboard Cite

Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is of great significance to target tracking and visual information mining. Aiming at the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information, and extract salient target contours by fusing Chan–Vese and local binary ﬁtting models. Then, the original underwater images are used to supplement the missing contour information by using the local image ﬁtting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours.

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Underwater Scene Segmentation by Deep Neural Network

Cited by 10 publications

References 0 publications

Semantic Segmentation of Underwater Imagery: Dataset and Benchmark

Semantic Segmentation of Underwater Imagery: Dataset and Benchmark

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Dual-fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

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