Intelligent Target Visual Tracking and Control Strategy for Open Frame Underwater Vehicles

Sun, Chaoyu; Wan, Zhaoliang; Huang, Hai; Zhang, Guocheng; Bao, Xuan; Li, Jiyong; Sheng, Mingwei; Yang, Xu

doi:10.1017/s0263574720001502

Cited by 8 publications

(4 citation statements)

References 29 publications

(29 reference statements)

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“…The integration of deep learning techniques into underwater vehicles for detection and identification represents a pivotal research domain in marine maintenance. Sun [1] introduced a kernelized correlation filter tracker and a novel fuzzy controller, which were trained using a deep learning model, resulting in favorable outcomes in visual tracking of underwater vehicles. Chu [10] utilized deep reinforcement learning based on a double-deep Q-network for autonomous underwater navigation, leading to effective path planning and obstacle avoidance.…”

Section: Related Workmentioning

confidence: 99%

“…Underwater target detection has a wide range of applications in marine environment monitoring and safety, and most existing target detection algorithms are implemented on Autonomous Underwater Vehicle (AUV). These AUV play a crucial role in enhancing marine monitoring and maintenance efforts [1]. Due to the influence of complex sea conditions and refraction of light transmission, the images obtained by AUV and other underwater vision equipment have weak features such as feature blur, loss and distortion.…”

Section: Introductionmentioning

confidence: 99%

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IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Cai,

Zhang,

et al. 2024

Robotica

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The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Cai,

Zhang,

et al. 2024

Robotica

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“…The KCF algorithm through the adaptive appearance model and its tracking strategy [28], feature fusion and scale correction mechanism [29] to deal with the challenges of underwater tracking. BACF is used to obtain more adaptive features, and combine scale estimation with the confidence -based update strategy to improve the performance of the tracker [30].…”

Section: Underwater Object Trackingmentioning

confidence: 99%

Adjust Sample Imbalance and Exclude Similar Object in Underwater Object Tracking

Li¹,

Wang²,

Li³

et al. 2023

Preprint

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Visual tracker includes network and post-processing. Despite the color distortion and low contrast of underwater images, advanced trackers can still be very competitive in underwater object tracking because deep learning empowers the networks to discriminate the appearance features of the target. However, underwater object tracking also faces another problem. Underwater targets such as fish and dolphins, usually appear in groups, and creatures of the same species usually have similar expressions of appearance features, so it is challenging to distinguish the weak differences characteristics only by the network itself. The existing detection-based post-processing only reflects the results of single frame detection, but cannot locate real targets among similar targets. In this paper, we propose a new post-processing strategy based on motion, which uses Kalman filter (KF) to maintain the motion information of the target and exclude similar targets around. Specifically, we use the KF predicted box and the candidate boxes in the response map and their confidence to calculate the candidate location score to find the real target. Our method does not change the network structure, nor does it perform additional training for the tracker. It can be quickly applied to other tracking fields with similar target problem. We improved SOTA trackers based on our method, and proved the effectiveness of our method on UOT100 and UTB180. The AUC of our method for OSTrack on similar subsequences is improved by more than 3% on average, and the precision and normalization precision are improved by more than 3.5% on average. It has been proved that our method has good compatibility in dealing with similar target problems and can enhance performance of the tracker together with other methods. More details can be found in: https://github.com/LiYunfengLYF/KF_in_underwater_trackers.

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“…Considering that the low-cost CF tracker is easier to apply to the underwater vehicle, the research tends to improve underwater trackers based on CF trackers. The classical KCF algorithm has been improved to adapt to UOT tasks using adaptive multi-appearance models and a new tracking strategy, 29 adding self-discrimination mechanism, 30 adding a fusion correction mechanism, 31 using feature fusion and scale adaptation, 32 and so on. In addition, Lu et al 33 adaptively fused the three features based on Background-Aware Correlation Filter (BACF) and added scale estimation and high confidence update strategy.…”

Section: Underwater Object Trackingmentioning

confidence: 99%

UStark: underwater image domain-adaptive tracker based on Stark

Huo

Liu

et al. 2022

J. Electron. Imag.

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. Though the open-air tracker has achieved an advanced level, its design is still a challenging task for degraded underwater images. Using underwater enhancement technology can improve the performance of underwater trackers. However, most underwater image enhancement methods focus on improving the visual effect rather than serving the tracker better. Therefore, we intended to explore a simple but powerful image domain-adaptive method to improve Stark’s performance by enhancing Stark’s input images. Specifically, it consists of underwater image adaptation network (UIAN) with double heads and adaptation block based on scene estimation (ABSE) that consists of three independent image processing modules without deep learning. UIAN is used to predict the category of image domain and parameters of ABSE. ABSE decodes the parameters and sequentially process underwater images in each module. The training of UIAN is independent of the training of the tracker. After training the class prediction head of UIAN first, freezing its weights, by initializing and tracking in one enhanced image and computing tracker’s loss, the parameter head can be trained to make sure UIANs hyperparameters can match the Stark tracker. The UStark proposed can adaptively process clear and degraded underwater images. Compared with Stark, UStark has improved the accuracy and success rate in typical underwater environments by 3.7% and 1.5% (blue), 5% and 3.4% (yellow), and 5.4% and 3.3% (dark), respectively. In addition, compared with other underwater image enhancement methods, our method can enhance the performance of the tracker in more categories of underwater images.

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Intelligent Target Visual Tracking and Control Strategy for Open Frame Underwater Vehicles

Cited by 8 publications

References 29 publications

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Adjust Sample Imbalance and Exclude Similar Object in Underwater Object Tracking

UStark: underwater image domain-adaptive tracker based on Stark

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