A Robust Object Segmentation Network for UnderWater Scenes

Chen, Ruizhe; Fu, Zhenqi; Huang, Yue; Cheng, En; Ding, Xinghao

doi:10.1109/icassp43922.2022.9746176

Cited by 3 publications

(10 citation statements)

References 14 publications

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“…As can be seen in Figure 1, the U-net (Ronneberger et al, 2015) is used as the backbone and VGG-16 (Simonyan and Zisserman, 2015) as the encoder of our proposed PSS-Net. The existing conventional models for semantic segmentation (Islam et al, 2020;Zhang et al, 2021;Chen et al, 2022) train both the background and foreground from the feature map extracted from one model. However, the PSS-Net proposed in this study extracts the background and foreground feature maps from different models respectively, and trains the background and foreground based on them.…”

Section: Model Architecture Of Pss-netmentioning

confidence: 99%

“…In case 1, the numbers of images in the train, validation, and test subsets are divided into the ratio of 6:2:2, including the background images, similarly to in a previous study . Moreover, in Case 2, images in the dataset are included, and it excludes the background images, which are divided into only the train and test subsets similar to in a previous study (Chen et al, 2022). In addition, the resolution of the input images was 352 × 352 pixels for both Cases 1 and 2.…”

Section: Experimental Datasetmentioning

confidence: 99%

“…Furthermore, many marine animals have protective colors (Ditria et al, 2020;Panaïotis et al, 2022). To solve these problems, previous studies have been actively conducted on convolutional neural network (CNN)based object detection (Pedersen et al, 2019;Berg et al, 2022;Chen et al, 2022). Object detection can be classified into two types: box-based and pixel-based object detection (where latter is semantic segmentation).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have concentrated on CNN model modifications for the same purpose (Islam et al, 2020;Zhang et al, 2021;Chen et al, 2022). A representative CNN model modification is to apply an attention mechanism (Zhang et al, 2021;Chen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…For example, the WaterSNet (Chen et al, 2022) used an attention fusion block (AFB) module, which employs an attention mechanism. The AFB module is adopted to utilize global context information.…”

Section: Introductionmentioning

confidence: 99%

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PSS-net: Parallel semantic segmentation network for detecting marine animals in underwater scene

Kim

Park²

2022

Front. Mar. Sci.

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Marine scene segmentation is a core technology in marine biology and autonomous underwater vehicle research. However, it is challenging from the perspective of having a different environment from that of the conventional traffic segmentation on roads. There are two major challenges. The first is the difficulty of searching for objects under seawater caused by the relatively low-light environment. The second problem is segmenting marine animals with protective colors. To solve such challenges, in previous research, a method of simultaneously segmenting the foreground and the background was proposed based on a simple modification of the conventional model; however, it has limitations in improving the segmentation accuracy. Therefore, we propose a parallel semantic segmentation network to solve the above issues in which a model and a loss are employed to locate the foreground and the background separately. The training task to locate the foreground and the background is reinforced in the proposed method by adding an attention technique in a parallel model. Furthermore, the final segmentation is performed by aggregating two feature maps obtained by separately locating the foreground and the background.The test results using an open dataset for marine animal segmentation reveal that the proposed method achieves performance of 87%, 97.3%, 88%, 95.2%, and 0.029 in the mean intersection of the union, structure similarities, weighted F-measure, enhanced-alignment measure, and mean absolute error, respectively. These findings confirm that the proposed method has higher accuracy than the state-of-the-art methods. The proposed model and code are publicly available via Github1.

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Section: Model Architecture Of Pss-netmentioning

confidence: 99%

Section: Experimental Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PSS-net: Parallel semantic segmentation network for detecting marine animals in underwater scene

Kim

Park²

2022

Front. Mar. Sci.

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MSGNet: multi-source guidance network for fish segmentation in underwater videos

Zhang,

Yu,

et al. 2023

Front. Mar. Sci.

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Fish segmentation in underwater videos provides basic data for fish measurements, which is vital information that supports fish habitat monitoring and fishery resources survey. However, because of water turbidity and insufficient lighting, fish segmentation in underwater videos has low accuracy and poor robustness. Most previous work has utilized static fish appearance information while ignoring fish motion in underwater videos. Considering that motion contains more detail, this paper proposes a method that simultaneously combines appearance and motion information to guide fish segmentation in underwater videos. First, underwater videos are preprocessed to highlight fish in motion, and obtain high-quality underwater optical flow. Then, a multi-source guidance network (MSGNet) is presented to segment fish in complex underwater videos with degraded visual features. To enhance both fish appearance and motion information, a non-local-based multiple co-attention guidance module (M-CAGM) is applied in the encoder stage, in which the appearance and motion features from the intra-frame salient fish and the moving fish in video sequences are reciprocally enhanced. In addition, a feature adaptive fusion module (FAFM) is introduced in the decoder stage to avoid errors accumulated in the video sequences due to blurred fish or inaccurate optical flow. Experiments based on three publicly available datasets were designed to test the performance of the proposed model. The mean pixel accuracy (mPA) and mean intersection over union (mIoU) of MSGNet were 91.89% and 88.91% respectively with the mixed dataset. Compared with those of the advanced underwater fish segmentation and video object segmentation models, the mPA and mIoU of the proposed model significantly improved. The results showed that MSGNet achieves excellent segmentation performance in complex underwater videos and can provide an effective segmentation solution for fisheries resource assessment and ocean observation. The proposed model and code are exposed via Github1.

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A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video

Böer

Gröger

Badri-Höher

et al. 2023

Sensors

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The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%.

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A Robust Object Segmentation Network for UnderWater Scenes

Cited by 3 publications

References 14 publications

PSS-net: Parallel semantic segmentation network for detecting marine animals in underwater scene

PSS-net: Parallel semantic segmentation network for detecting marine animals in underwater scene

MSGNet: multi-source guidance network for fish segmentation in underwater videos

A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video

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