Underwater Object Classification Method Based on Depthwise Separable Convolution Feature Fusion in Sonar Images

Gong, Wenjing; Tian, Jie; Liu, Jiyuan

doi:10.3390/app12073268

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Ensuring the detection accuracy of this model, the depth-separable convolution [43] was introduced in the backbone network instead of the 3 × 3 ordinary convolution in the backbone network, which reduced the number of parameters and computation in the network model and improved the detection speed. The depth-separable convolution structure is shown in Figure 6.…”

Section: Depth Separable Convolutionmentioning

confidence: 99%

Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

et al. 2023

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During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the conveyor belt, resulting in tears and fractures, which affect transportation efficiency and production safety. In this paper, we propose a lightweight target detection algorithm, GhostNet-CBAM-YOLOv4, to resolve the problem of the difficulty of detecting foreign objects at high-speed movement in an underground conveyor belt. The Kmeans++ clustering method was used to preprocess the data set to obtain the anchor box suitable for the foreign object size. The GhostNet lightweight module replaced the backbone network, reducing the model’s parameters. The CBAM attention module was introduced to enhance the ability of feature extraction facing the complex environment under the mine. The depth separable convolution was used to simplify the model structure and reduce the number of parameters and calculations. The detection accuracy of the improved method on the foreign body data set reached 99.32%, and the detection rate reached 54.7 FPS, which was 6.83% and 42.1% higher than the original YOLOv4 model, respectively. The improved method performed better than the original model on the other two datasets and could effectively avoid misdetection and omission detection. In comparison experiments with similar methods, our proposed method also demonstrated good performance, verifying its effectiveness.

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Section: Depth Separable Convolutionmentioning

confidence: 99%

Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

et al. 2023

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“…Underwater targets such as wrecks or other man-made objects will sometimes protrude above the seafloor, and thus cast a recognizable shadow [42]. Objects different in composition than the bottom surface will stand out from the surroundings and present a change in the acoustic backscatter.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Side-Scan Sonar Resolution and Acoustic Shadow Phenomenon on the Quality of Sonar Imagery and Data Interpretation Capabilities

Grządziel

2023

Remote Sensing

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Side-scan sonar is designed and used for a variety of survey work, in both military and civilian fields. These systems provide acoustic imageries that play a significant role in a variety of marine and inland applications. For this reason, it is extremely important that the recorded sonar image is characterized by high resolution, detail and sharpness. This article is mainly aimed at the demonstration of the impact of side-scan sonar resolution on the imaging quality. The article also presents the importance of acoustic shadow in the process of analyzing sonar data and identifying underwater objects. The real measurements were carried out using two independent survey systems: hull-mounted sonar and towed side-scan sonar. Six different shipwrecks lying in the Baltic Sea were selected as the objects of research. The results presented in the article also constitute evidence of how the sonar technology has changed over time. The survey findings show that by maintaining the appropriate operational conditions and meeting several requirements, it is possible to obtain photographic-quality sonar images, which may be crucial in the process of data interpretation and shipwreck identification.

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Deep Learning Algorithms for Sonar Imagery Analysis and Its Application in Aquaculture: A Review

Chai,

Yu,

et al. 2023

IEEE Sensors J.

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Underwater Object Classification Method Based on Depthwise Separable Convolution Feature Fusion in Sonar Images

Cited by 5 publications

References 25 publications

Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

The Impact of Side-Scan Sonar Resolution and Acoustic Shadow Phenomenon on the Quality of Sonar Imagery and Data Interpretation Capabilities

Deep Learning Algorithms for Sonar Imagery Analysis and Its Application in Aquaculture: A Review

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