Color Image Segmentation of Live Grouper Fish with Complex Background in Seawater

Ma, Guoqiang; Tian, Yunchen; Li, X.L.; Xing, Kun; Xu, Su

doi:10.4028/www.scientific.net/amm.743.293

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Fish segmentation using noncontact phenotypic segmentation methods was previously studied primarily based on the low-order visual information of image pixels in fuzzy segmentation algorithms (Otsu, 1979), such as image information and pixel extraction using color space conversion, color component extraction, and median filter processing (Kitschier et al, 2011;Cherkassky and Ma, 2004;van den Heuvel et al, 2008;Saifullah et al, 2021). Ma et al (2016) divided the color image vector-valued pixel points into three singlechannel pixel points (R, G, and B) and used a K-means clustering algorithm to obtain the fish phenotypes. Chen Y. Y. et al (2019) implemented a shape feature extraction method based on Fisher's discriminant function to segment fish and backgrounds quickly.…”

Section: Phenotype Segmentation Methods Based On Low-level Visual Inf...mentioning

confidence: 99%

RA-UNet: an intelligent fish phenotype segmentation method based on ResNet50 and atrous spatial pyramid pooling

Li,

Liu,

Yang

et al. 2023

Front. Environ. Sci.

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Introduction: Changes in fish phenotypes during aquaculture must be monitored to improve the quality of fishery resources. Therefore, a method for segmenting and measuring phenotypes rapidly and accurately without harming the fish is essential. This study proposes an intelligent fish phenotype segmentation method based on the residual network, ResNet50, and atrous spatial pyramid pooling (ASPP).Methods: A sufficient number of fish phenotypic segmentation datasets rich in experimental research was constructed, and diverse semantic segmentation datasets were developed. ResNet50 was then built as the backbone feature extraction network to prevent the loss of fish phenotypic feature information and improve the precision of fish phenotypic segmentation. Finally, an ASPP module was designed to improve the phenotypic segmentation accuracy of different parts of fish.Results: The test algorithm based on the collected fish phenotype segmentation datasets showed that the proposed algorithm (RA-UNet) yielded the best results among several advanced semantic segmentation models. The mean intersection over union (mIoU) and mean pixel accuracy (mPA) were 87.8% and 92.3%, respectively.Discussion: Compared with the benchmark UNet algorithm, RA-UNet demonstrated improvements in the mIoU and mPA by 5.0 and 1.8 percentage points, respectively. Additionally, RA-UNet exhibited superior fish phenotype segmentation performance, with a low false detection rate and clear and complete edge segmentation. Conclusively, the RA-UNet proposed in this study has high accuracy and edge segmentation ability and can, therefore, directly improve the efficiency of phenotypic monitoring in fish farming.

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Section: Phenotype Segmentation Methods Based On Low-level Visual Inf...mentioning

confidence: 99%

RA-UNet: an intelligent fish phenotype segmentation method based on ResNet50 and atrous spatial pyramid pooling

Li,

Liu,

Yang

et al. 2023

Front. Environ. Sci.

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show abstract

“…Common color spaces include YUV, Lab, and HSV space. The HSV color space is more akin to the human emotional perception of color [33,34], which encapsulates three pieces of information about the hue, brightness, and saturation of the color. Therefore, the pixels in the main body of COT image are transformed from RGB space to HSV space.…”

Section: Color Space Transformationmentioning

confidence: 99%

Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

Liu

Zhang

Zhou

et al. 2021

International Journal of Photoenergy

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The microstructure is the key factor for quality discriminate of coke. In view of the characteristics of coke optical tissue (COT), a segmentation method of coke microstructures based on adaptive clustering was proposed. According to the strategy of multiresolution, adaptive threshold binarization and morphological filtering were carried out on COT images with lower resolution. The contour of the COT body was detected through the relationship checking between contours in the binary image, and hence, COT pixels were picked out to cluster for tissue segmentation. In order to get the optimum segmentation for each tissue, an advanced K -means method with adaptive clustering centers was provided according to the Calinski-Harabasz score. Meanwhile, Euclidean distance was substituted with Mahalanobis distance between each pixel in HSV space to improve the accuracy. The experimental results show that compared with the traditional K -means algorithm, FCM algorithm, and Meanshift algorithm, the adaptive clustering algorithm proposed in this paper is more accurate in the segmentation of various tissue components in COT images, and the accuracy of tissue segmentation reaches 94.3500%.

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“…In contrast to conventional 2 of 20 methods, computer vision technology offers unparalleled advantages in measuring nonlinear relationships, perimeters, areas, quantities, colors, and chemical compositions of the target object [6][7][8][9]. Ma [10] utilized the K-means clustering algorithm to segment color images containing groupers. Through segmentation experiments on 100 artificial seawater images of groupers in RGB format, they achieved a high accuracy of 98 blue components.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Accurate Crayfish Sorting by Size and Maturity Based on Improved YOLOv5

Liu

Zhang

et al. 2023

Applied Sciences

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In response to the issues of high-intensity labor, low efficiency, and potential damage to crayfish associated with traditional manual sorting methods, an automated and non-contact sorting approach based on an improved YOLOv5 algorithm is proposed for the rapid sorting of crayfish maturity and size. To address the difficulty in focusing on small crayfish, the Backbone is augmented with Coordinate Attention to boost its capability to extract features. Additionally, to address the difficulty in achieving high overall algorithm efficiency and reducing feature redundancy, the Bottleneck Transformer is integrated into both the Backbone and Neck, which improves the accuracy, generalization performance, and the model’s computational proficiency. The dataset of 3464 images of crayfish collected from a crayfish breeding farm is used for the experiments. The dataset is partitioned randomly, with 80% of the data used for training and the remaining 20% used for testing. The results indicate that the proposed algorithm achieves an mAP of 98.8%. Finally, the model is deployed using TensorRT, and the processing time for an image is reduced to just 2 ms, which greatly improves the processing speed of the model. In conclusion, this approach provides an accurate, efficient, fast, and automated solution for crayfish sorting.

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Color Image Segmentation of Live Grouper Fish with Complex Background in Seawater

Cited by 4 publications

References 5 publications

RA-UNet: an intelligent fish phenotype segmentation method based on ResNet50 and atrous spatial pyramid pooling

RA-UNet: an intelligent fish phenotype segmentation method based on ResNet50 and atrous spatial pyramid pooling

Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

Rapid and Accurate Crayfish Sorting by Size and Maturity Based on Improved YOLOv5

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