Real-Time Detecting Method of Marine Small Object with Underwater Robot Vision

Xu, Fengqiang; Ding, Xueyan; Peng, Jinjia; Yuan, Guoliang; Zhang, Jun; Fu, Xianping

doi:10.1109/oceanskobe.2018.8558804

Cited by 21 publications

(8 citation statements)

References 12 publications

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“…These categories were sea cucumber, sea urchin and scallop. The results obtained were quite good for the authors, achieving higher accuracy of 79.6% [38]. A novel filter framework which can solve any path of problems related to tracking was proposed by Lei Sun et al 2018.…”

Section: Section Iii: the Basic Point Of A Reviewmentioning

confidence: 84%

The Object Detection, Perspective and Obstacles In Robotic: A Review

Khan

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

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Section: Section Iii: the Basic Point Of A Reviewmentioning

confidence: 84%

The Object Detection, Perspective and Obstacles In Robotic: A Review

Khan

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

show abstract

“…There are two main counting means in aquaculture based on computer vision applied to stock estimation or biomass estimation: image processing and video analysis for counting. (Atienza‐Vanacloig, Andreu‐García, López‐García, Valiente‐González, & Puig‐Pons, 2016; Le & Xu, 2017; Xu et al, 2018). The specific methods are shown in Table 1.…”

Section: Counting Methods Based On Computer Vision Technologymentioning

confidence: 99%

Automatic counting methods in aquaculture: A review

Miao

Peng

et al. 2020

J World Aquaculture Soc

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Object counting in aquaculture is an important task, and has been widely applied in fish population estimation, estimation of lobster abundance and scallop stocks, and so forth. However, underwater object counting is challenging for biologists and marine scientists because of the diversity of backgrounds of the lake or ocean, the uncertainty of the object motion, and the occlusion between objects. With the rapid development of sensor, computer vision, and acoustic technologies, advanced and efficient counting methods are available in aquaculture. We reviewed underwater object counting methods in aquaculture, provided a survey including more than 50 articles in the recent 10 years, and analyzed the pros and cons of the counting methods and the applicable scenarios of those methods. Finally, the major challenges and future trends of underwater object counting in aquaculture are discussed.

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“…Image enhancement is a necessary data augmentation method and has been widely used in underwater target detection tasks. A histogram-based image enhancement was reported for the pre-processing of underwater images [ 22 , 23 ]. Huang demonstrated that image enhancement can effectively improve the detection performance of the YOLO v5 in natural scenes [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements

Wang

et al. 2023

Sensors

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Recently, rapidly developing artificial intelligence and computer vision techniques have provided technical solutions to promote production efficiency and reduce labor costs in aquaculture and marine resource surveys. Traditional manual surveys are being replaced by advanced intelligent technologies. However, underwater object detection and recognition are suffering from the image distortion and degradation issues. In this work, automatic monitoring of sea cucumber in natural conditions is implemented based on a state-of-the-art object detector, YOLOv7. To depress the image distortion and degradation issues, image enhancement methods are adopted to improve the accuracy and stability of sea cucumber detection across multiple underwater scenes. Five well-known image enhancement methods are employed to improve the detection performance of sea cucumber by YOLOv7 and YOLOv5. The effectiveness of these image enhancement methods is evaluated by experiments. Non-local image dehazing (NLD) was the most effective in sea cucumber detection from multiple underwater scenes for both YOLOv7 and YOLOv5. The best average precision (AP) of sea cucumber detection was 0.940, achieved by YOLOv7 with NLD. With NLD enhancement, the APs of YOLOv7 and YOLOv5 were increased by 1.1% and 1.6%, respectively. The best AP was 2.8% higher than YOLOv5 without image enhancement. Moreover, the real-time ability of YOLOv7 was examined and its average prediction time was 4.3 ms. Experimental results demonstrated that the proposed method can be applied to marine organism surveying by underwater mobile platforms or automatic analysis of underwater videos.

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Real-Time Detecting Method of Marine Small Object with Underwater Robot Vision

Cited by 21 publications

References 12 publications

The Object Detection, Perspective and Obstacles In Robotic: A Review

The Object Detection, Perspective and Obstacles In Robotic: A Review

Automatic counting methods in aquaculture: A review

In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements

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