Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model

Goutcher, Ross; Barrington, Christian; Hibbard, Paul B.; Graham, Bruce

doi:10.1167/jov.21.7.13

Cited by 4 publications

(3 citation statements)

References 104 publications

(107 reference statements)

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“…Therefore, the lightweight DL model with high efficiency is also one of the future research directions. DL model for non‐2D images. Most DL models are still based on 2D images, but the binocular vision 200 and depth camera 201 provide technical support for 3D information acquisition. The acquired 3D information can help researchers to further study the behaviour of aquatic animals such as feeding behaviour, illness behaviour 179 and so on.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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The ocean is an important ecosystem, and aquatic animals play an important role in the biological world, especially in aquaculture. How to accurately and intelligently recognise and detect aquatic animals is one of the urgent problems in the field of underwater biological detection. The wide applications of artificial intelligence (AI), especially deep learning (DL), provide new opportunities and challenges for the efficient and intelligent exploration of aquatic animals. DL has been widely used in the visual recognition and detection of terrestrial animals, but it is in the early stages of use for aquatic animals due to the complexity of underwater environment and the difficulty of data acquisition. Here, this article reviews the current application status of DL for aquatic animals, potential challenges and future directions. The key advances of DL algorithms applied to the visual recognition and detection of aquatic animals are generalised, including datasets, algorithms and performance. The applications of DL are summarised in aquatic animals, including image detection, video detection, species classification, biomass estimation, behaviour analysis and food safety. Furthermore, the challenges are summed up and classified in the object recognition and detection domain for aquatic animals. Finally, further research direction is discussed and the conclusions are drawn. The key advances of DL in the recognition and detection of aquatic animals will help to further excavate and extend the application of DL in the field of marine biological exploration.

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Section: Discussionmentioning

confidence: 99%

Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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“…Existing research work shows that the spatial resolution of the camera arrays can be improved by deep learning or image fusion with the help of the relationship between the images captured by different cameras [ 10 , 11 ]. Image fusion combines two or more images of the same object into a single image that is more easily interpreted than any of the originals [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Image fusion of multi-vision system with one high resolution image for the measurement of long distance

Liu¹,

Chu²,

Ren³

et al. 2023

Heliyon

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“…There was a position gap between the pixels of the left and right images, which is a disparity. By calculating the disparity between corresponding points in the image, three-dimensional information about the object could be obtained using the triangulation principle [30]. The model of using stereo vision to calculate the threedimensional coordinates of target feature points is shown in Figure 5.…”

Section: Binocular Stereo Vision Measurement Algorithm 41 Three-dimen...mentioning

confidence: 99%

Evaluation of Real-Time Perception of Deformation State of Host Rocks in Coal Mine Roadways in Dusty Environment

et al. 2023

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Intelligent mining needs to achieve real-time acquisition of surrounding rock deformation data of roadways and analysis and provide technical support for intelligent mining construction. To solve problems such as significant error, information lag, and low acquisition frequency of surrounding rock monitoring technology at the current stage, a perception method, RSBV of roadway deformation situation, based on binocular vision is proposed, which realizes the dynamic, accurate real-time acquisition of host rocks’ relative deformation in a dusky environment. The low illumination image enhancement method is used to preprocess original images, which reduces the impact of low illumination and high dust; the K-medoids algorithm segments the target image, and the SIFT algorithm extracts feature points from the ROI (region of interest). The influence of eliminating background images on the feature point extraction is revealed, and the efficiency of feature extraction is improved; the method of SIFT feature-matching with epipolar constraints is studied, which improves the accuracy and speed of feature points. The roadway deformation characteristics are analyzed, and the reflective target is used as the monitoring point. A roadway deformation acquisition and analysis platform based on binocular vision is built in a dim environment. Zhang’s method is selected to calibrate the camera parameters, and stereo rectification is carried out for the target motion image. The adaptability of the RSBV method to different surrounding rock deformation scales is studied and compared with the measurement results of the SGBM algorithm. The results show that the error of the RSBV method is controlled within 1.6%, which is 2.88% lower than the average error of the SGBM algorithm. The average time for processing a group of binocular images is 1.87 s, which is only 20% of the SGBM algorithm. The research result provides a reliable theoretical basis for the real-time and accurate evaluation of the surrounding rock deformation mechanism.

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Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model

Cited by 4 publications

References 104 publications

Deep learning for visual recognition and detection of aquatic animals: A review

Deep learning for visual recognition and detection of aquatic animals: A review

Image fusion of multi-vision system with one high resolution image for the measurement of long distance

Evaluation of Real-Time Perception of Deformation State of Host Rocks in Coal Mine Roadways in Dusty Environment

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