Automated Synthetic Aperture Sonar Image Segmentation using Spatially Coherent Clustering

Steele, Shannon-Morgan; Ejdrygiewicz, Jillian; Dillon, Jeremy

doi:10.23919/oceans44145.2021.9705933

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…SUN et al [12] used a probabilistic neural network (PNN) for seabed sediment classification based on SSS imagery, with high computational and spatial complexity. Steele [13] proposed an unsupervised segmentation technique for accurate and interpretable seabed imagery mapping. The experiments showed that spatially coherent clustering could significantly increase segmentation accuracy relative to OpenCV K-means and ArcGIS Pro iterative self-organizing (ISO) clustering (up to 15% and 20%, respectively).…”

Section: Related Work 21 About Seabed Sedimentmentioning

confidence: 99%

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A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C

Chen

Shi

2023

JMSE

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Semantic segmentation methods have been successfully applied in seabed sediment detection. However, fast models like YOLO only produce rough segmentation boundaries (rectangles), while precise models like U-Net require too much time. In order to achieve fast and precise semantic segmentation results, this paper introduces a novel model called YOLO-C. It utilizes the full-resolution classification features of the semantic segmentation algorithm to generate more accurate regions of interest, enabling rapid separation of potential targets and achieving region-based partitioning and precise object boundaries. YOLO-C surpasses existing methods in terms of accuracy and detection scope. Compared to U-Net, it achieves an impressive 15.17% improvement in mean pixel accuracy (mPA). With a processing speed of 98 frames per second, YOLO-C meets the requirements of real-time detection and provides accurate size estimation through segmentation. Furthermore, it achieves a mean average precision (mAP) of 58.94% and a mean intersection over union (mIoU) of 70.36%, outperforming industry-standard algorithms such as YOLOX. Because of the good performance in both rapid processing and high precision, YOLO-C can be effectively utilized in real-time seabed exploration tasks.

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Section: Related Work 21 About Seabed Sedimentmentioning

confidence: 99%

“…Here, n represents the total number of categories. (12) mPA is utilized to assess the average ratio of accurately predicted pixels for each class, as shown in Formula (13).…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C

Chen

Shi

2023

JMSE

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Deep Multi-Look Sequence Processing for Synthetic Aperture Sonar Image Segmentation

Gerg

Monga

2023

IEEE Trans. Geosci. Remote Sensing

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DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation

et al. 2022

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Side-scan sonar systems play an important role in tasks such as marine terrain exploration and underwater target identification. Target segmentation of side-scan sonar images is an effective method of underwater target detection. However, the principle of side-scan sonar systems leads to high noise interference, weak boundary information, and difficult target feature extraction of sonar images. To solve these problems, we propose a Double Split Attention (DSA) SOLO. Specially, we present an efficient attention module called DSA which fuses spatial attention and channel attention together effectively. DSA first splits feature maps into two parts along channel dimensions before processing them in parallel. Next, DSA utilizes C-S Unit and S-C Unit to describe relevant features in the spatial and channel dimensions, respectively. After that, the results of the two parts are aggregated to improve feature representation. We embedded the proposed DSA module after the FPN network of SOLOv2, and this approach improves the instance segmentation accuracy to a great extent. Experimental results show that our proposed DSA-SOLO on SCTD dataset achieves 78.4% mAP.5, which is 5.1% higher than SOLOv2.

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Automated Synthetic Aperture Sonar Image Segmentation using Spatially Coherent Clustering

Cited by 3 publications

References 13 publications

A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C

A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C

Deep Multi-Look Sequence Processing for Synthetic Aperture Sonar Image Segmentation

DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation

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