An Unmixing-Based Network for Underwater Target Detection From Hyperspectral Imagery

Qi, Jiahao; Gong, Zhiqiang; Xue, Wei; Liu, Xingyue; Yao, Aihuan

doi:10.1109/jstars.2021.3080919

Cited by 20 publications

(6 citation statements)

References 41 publications

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“…The former focuses on improving the quality of degraded underwater images caused by light dispersion and color distortion, while the latter aims to locate and identify underwater targets more accurately and efficiently. Various image enhancement techniques have been investigated in the literature for improving contrast, correcting color shifts, and sharpening edges in underwater images [7][8][9]. Furthermore, other studies aim to counteract the negative effects of image blur by enhancing the network architecture [10][11][12] and refining training strategies [13].…”

Section: Underwater Object Detectionmentioning

confidence: 99%

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

Zhao,

Yun,

Yuan

et al. 2023

JMSE

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Underwater target detection plays a crucial role in marine environmental monitoring and early warning systems. It involves utilizing optical images acquired from underwater imaging devices to locate and identify aquatic organisms in challenging environments. However, the color deviation and low illumination in these images, caused by harsh working conditions, pose significant challenges to an effective target detection. Moreover, the detection of numerous small or tiny aquatic targets becomes even more demanding, considering the limited storage and computing power of detection devices. To address these problems, we propose the YOLOv7-CHS model for underwater target detection, which introduces several innovative approaches. Firstly, we replace efficient layer aggregation networks (ELAN) with the high-order spatial interaction (HOSI) module as the backbone of the model. This change reduces the model size while preserving accuracy. Secondly, we integrate the contextual transformer (CT) module into the head of the model, which combines static and dynamic contextual representations to effectively improve the model’s ability to detect small targets. Lastly, we incorporate the simple parameter-free attention (SPFA) module at the head of the detection network, implementing a combined channel-domain and spatial-domain attention mechanism. This integration significantly improves the representation capabilities of the network. To validate the implications of our model, we conduct a series of experiments. The results demonstrate that our proposed model achieves higher mean average precision (mAP) values on the Starfish and DUO datasets compared to the original YOLOv7, with improvements of 4.5% and 4.2%, respectively. Additionally, our model achieves a real-time detection speed of 32 frames per second (FPS). Furthermore, the floating point operations (FLOPs) of our model are 62.9 G smaller than those of YOLOv7, facilitating the deployment of the model. Its innovative design and experimental results highlight its effectiveness in addressing the challenges associated with underwater object detection.

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Section: Underwater Object Detectionmentioning

confidence: 99%

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

Zhao,

Yun,

Yuan

et al. 2023

JMSE

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“…Among these resources, oil and natural gas production has reached one-fourth of global production. Also, the ocean is rich in nickel, cobalt, and other mineral resources, with much higher content than land [2]. However, humans have only exploited about 5% of the ocean.…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Processing and Target Detection from Particle Swarm Optimization Algorithm

Zhang,

Bi,

2023

Preprint

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The underwater image obtained is difficult to satisfy human visual perception because of the particle scattering and water absorption phenomena when visible light propagates underwater. In underwater images, light absorption easily leads to image distortion and reduction of image contrast and brightness. Therefore, this work aims to improve the quality of underwater image processing, reduce the distortion rate of underwater images, and further improve the efficiency of underwater image extraction, processing, and tracking. This work combines intelligent blockchain technology in emerging multimedia industries with existing image processing technology to improve the target detection capability of image processing algorithms. Firstly, the theory of visual saliency analysis (VSA) is studied. The steps of image processing using VSA are analyzed. Based on the original Itti model, the visual significance detection step is optimized. Then, the theoretical basis and operation steps of particle swarm optimization (PSO) algorithm in intelligent blockchain technology are studied. VSA theory is combined with PSO to design underwater image processing algorithms and target detection optimization algorithms for underwater images. The experimental results show that: (1) the method has a higher F value and lower Mean Absolute Error. (2) Compared with the original image, the restored image entropy through this method is greatly improved, and the information in the image increases. Therefore, this method has good performance. Besides, this method performs well in image definition, color, and brightness. The quality of the restored image through this method is better than that of other algorithms. (3) Compared with similar algorithms, the relative errors of this method are reduced by 2.56%, 3.24% and 3.89%, respectively. The results show that the method has high accuracy. The research results can provide a reference for future underwater image processing and target detection research. In addition, the designed underwater image processing and target detection and tracking algorithms can improve the detection efficiency and accuracy of underwater targets and help to accurately obtain underwater target images.

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“…In recent years, with the increasingly large scale of development and utilization of marine resources, underwater optical visual target detection with extensive application scenarios has played an increasingly important role in the field of underwater security [1], marine exploration [2], fish farming [3], and marine ecology [4]. Therefore, achieving underwater autonomous operation through visual target detection by using underwater optical images has become a research hotspot in the field of computer vision.…”

Section: Introductionmentioning

confidence: 99%

A Marine Organism Detection Framework Based on Dataset Augmentation and CNN-ViT Fusion

Jiang

Zhang

Pan

et al. 2023

JMSE

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Underwater vision-based detection plays an important role in marine resources exploration, marine ecological protection and other fields. Due to the restricted carrier movement and the clustering effect of some marine organisms, the size of some marine organisms in the underwater image is very small, and the samples in the dataset are very unbalanced, which aggravate the difficulty of vision detection of marine organisms. To solve these problems, this study proposes a marine organism detection framework with a dataset augmentation strategy and Convolutional Neural Networks (CNN)-Vision Transformer (ViT) fusion model. The proposed framework adopts two data augmentation methods, namely, random expansion of small objects and non-overlapping filling of scarce samples, to significantly improve the data quality of the dataset. At the same time, the framework takes YOLOv5 as the baseline model, introduces ViT, deformable convolution and trident block in the feature extraction network, and extracts richer features of marine organisms through multi-scale receptive fields with the help of the fusion of CNN and ViT. The experimental results show that, compared with various one-stage detection models, the mean average precision (mAP) of the proposed framework can be improved by 27%. At the same time, it gives consideration to both performance and real-time, so as to achieve high-precision real-time detection of the marine organisms on the underwater mobile platform.

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An Unmixing-Based Network for Underwater Target Detection From Hyperspectral Imagery

Cited by 20 publications

References 41 publications

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

Underwater Image Processing and Target Detection from Particle Swarm Optimization Algorithm

A Marine Organism Detection Framework Based on Dataset Augmentation and CNN-ViT Fusion

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