Radon Transform Detection Method for Underwater Moving Target Based on Water Surface Characteristic Wave

Man, 徐曼 Xu; Su, 裘溯 Qiu; Weiqi, 金伟其 Jin; Jie, 杨洁 Yang; Hong, 郭宏 Guo

doi:10.3788/aos201939.1001003

Cited by 4 publications

(2 citation statements)

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“…Traditional underwater target detection algorithms first digitize the images and then analyze them by modeling them with statistical learning theory to finally complete the detection task. One of the representatives of traditional underwater target detection is the surface feature ripple extraction underwater target detection algorithm proposed by Xu et al (2019), which models the photoelectric polarization image and then performs underwater target detection. However, this algorithm presents different shapes when imaging at different angles, resulting in a complex mathematical model that cannot be applied in real underwater scenarios.…”

Section: Related Workmentioning

confidence: 99%

Vision-based underwater target real-time detection for autonomous underwater vehicle subsea exploration

Zhou

Guo

et al. 2023

Front. Mar. Sci.

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Autonomous underwater vehicles (AUVs) equipped with online visual inspection systems can detect underwater targets during underwater operations, which is of great significance to subsea exploration. However, the undersea scene has some instinctive challenging problems, such as poor lighting conditions, sediment burial, and marine biofouling mimicry, which makes it difficult for traditional target detection algorithms to achieve online, reliable, and accurate detection of underwater targets. To solve the above issues, this paper proposes a real-time object detection algorithm for underwater targets based on a lightweight convolutional neural network model. To improve the imaging quality of underwater images, contrast limited adaptive histogram equalization with the fused multicolor space (FCLAHE) model is designed to enhance the image quality of underwater targets. Afterwards, a spindle-shaped backbone network is designed. The inverted residual block and group convolutions are used to extract depth features to ensure the target detection accuracy on one hand and to reduce the model parameter volume on the other hand under complex scenarios. Through extensive experiments, the precision, recall, and mAP of the proposed algorithm reached 91.2%, 90.1%, and 88.3%, respectively. It is also noticeable that the proposed method has been integrated into the embedded GPU platform and deployed in the AUV system in the practical scenarios. The average computational time is 0.053s, which satisfies the requirements of real-time object detection.

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Section: Related Workmentioning

confidence: 99%

Vision-based underwater target real-time detection for autonomous underwater vehicle subsea exploration

Zhou

Guo

et al. 2023

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…Cutter et al [19] employed Haar-like features and multiple cascaded classifiers to detect fish objects, while Rizzini et al [20] identified underwater objects based on the uniformity of underwater image color and sharpness information from contours. Qiu et al [21] proposed an algorithm based on surface feature ripples for detecting underwater moving objects in photopolarimetric imaging mode, which has become a notable example of traditional algorithms in underwater object detection. However, traditional detection methods require the design of various feature extraction models and rely on machine learning techniques for classification.…”

Section: Introductionmentioning

confidence: 99%

Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution

Yang,

Liang,

Zhou

et al. 2023

JMSE

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Cultural artifacts found underwater are located in complex environments with poor imaging conditions. In addition, the artifacts themselves present challenges for automated object detection owing to variations in their shape and texture caused by breakage, stacking, and burial. To solve these problems, this paper proposes an underwater cultural object detection algorithm based on the deformable deep aggregation network model for autonomous underwater vehicle (AUV) exploration. To fully extract the object feature information of underwater objects in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. In addition, the approach also incorporates a BAM module for feature optimization, which enhances the potential feature information of the object while weakening the background interference. Finally, the object prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validated and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, our method has been practically deployed on an AUV. In the field testing over a shipwreck site, the artifact detection frame rate reached up to 18 fps, which satisfies the real-time object detection requirement.

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Marine Cultural Target Detection for Underwater Robotics Based on Deep Aggregation Network with Deformation Convolution

Yang,

Liang,

Zhou

et al. 2023

Preprint

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Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

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Radon Transform Detection Method for Underwater Moving Target Based on Water Surface Characteristic Wave

Cited by 4 publications

References 0 publications

Vision-based underwater target real-time detection for autonomous underwater vehicle subsea exploration

Vision-based underwater target real-time detection for autonomous underwater vehicle subsea exploration

Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution

Marine Cultural Target Detection for Underwater Robotics Based on Deep Aggregation Network with Deformation Convolution

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