Underwater object detection and tracking based on multi-beam sonar image processing

Li, Min; Ji, Houwei; Wang, Xiangcun; Weng, Liyuan; Zhang, Gong

doi:10.1109/robio.2013.6739606

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…A Gabor filter is used to enhance the contrast of sonar images and a Kalman filter is used for tracking. Li et al [7] proposed a framework to analyze multibeam sonar images to detect and track objects in water. This method used improved Otsu segmentation to detect objects.…”

Section: Tracking Technologiesmentioning

confidence: 99%

CONet: A Cognitive Ocean Network

Wang

et al. 2019

IEEE Wireless Commun.

156

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The scientific and technological revolution of the Internet of Things has begun in oceanography.Historically, humans have observed the ocean from outside the ocean to study it. In recent years, changes have been made to the interior of the ocean, and laboratories have been built on the seafloor.Approximately 70.8% of the Earth is covered by ocean and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean resource exploration, and underwater environmental monitoring. Different from traditional wireless sensor networks, the Ocean of Things has its own unique features, such as low reliability and narrow bandwidth. These features may be great challenges for the Ocean of Things. Furthermore, the integration of the Ocean of Things with artificial intelligence has become a topic of increasing interest for oceanology research fields. The Cognitive Ocean Network (CONet) will become the mainstream of future ocean science and engineering development. In this paper, we define CONet. The contributions of our paper are as follows: (1) a CONet architecture is proposed and described in detail; (2) important and useful demo applications of CONet are proposed; and (3) future trends in CONet research are presented.

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Section: Tracking Technologiesmentioning

confidence: 99%

CONet: A Cognitive Ocean Network

Wang

et al. 2019

IEEE Wireless Commun.

156

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“…Compared with [ 22 ], which also employs the Otsu segmentation on multi-beam sonar images for feature detection, our presented work is novel in applying a power-law transform before performing different threshold segmentation methods (TSM), and it has been figured out that the best thresholding results are achieved when y = 0.1 x 1.415 , in this way the contrast between object and background is enlarged and the feature is highlighted, since the gray levels of the object are increased and the background ones are decreased. Therefore, the improved Otsu TSM has more accurate segmentation performance than the methodology used in [ 22 ]. Besides, [ 22 ] does not consider the computational time of their method, but our proposed segmentation approach could keep good tradeoff between segmentation precision and computational cost.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the improved Otsu TSM has more accurate segmentation performance than the methodology used in [ 22 ]. Besides, [ 22 ] does not consider the computational time of their method, but our proposed segmentation approach could keep good tradeoff between segmentation precision and computational cost. In [ 23 ], the classic Otsu thresholding and static thresholding are applied for object detection using the sector scanning sonar.…”

Section: Related Workmentioning

confidence: 99%

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

Yuan

Martínez

Eckert

et al. 2016

Sensors

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The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments.

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“…The experimental results show that the PF has robustness in target tracking. Later, Li et al [11] adopted an improved Otsu method to detect the underwater target, and then carried out underwater target tracking through the PF algorithm combined with a multifeature adaptive fusion strategy. However, the PF suffers from a high computation cost, because it requires a larger number of particles to represent the posterior density of the object state.…”

Section: Introductionmentioning

confidence: 99%

An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking

Wang

Zhao

et al. 2018

Applied Sciences

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To obtain accurate underwater target tracking results, an improved kernelized correlation filter (IKCF) algorithm is proposed to track the target in forward-looking sonar image sequences. Specifically, a base sample with a dynamically continuous scale is first applied to solve the poor performance of fixed-scale filters. Then, in order to prevent the filter from drifting when the target disappears and appears again, an adaptive filter update strategy with the peak to sidelobe ratio (PSR) of the response diagram is developed to solve the following target tracking errors. Finally, the experimental results show that the proposed IKCF can obtain accurate tracking results for the underwater targets. Compared to other algorithms, the proposed IKCF has obvious superiority and effectiveness.

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Underwater object detection and tracking based on multi-beam sonar image processing

Cited by 13 publications

References 9 publications

CONet: A Cognitive Ocean Network

CONet: A Cognitive Ocean Network

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking

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