Fish detection using morphological approach based-on k-means segmentation

Saifullah, Shoffan; Suryotomo, Andiko Putro; Yuwono, Bambang Darmo

doi:10.28989/compiler.v10i1.946

Cited by 6 publications

(5 citation statements)

References 26 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.…”

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

“…RGB images have three color channels, namely red (R), green (G), and blue (B). The conversion process uses (1) [31] to get a gray level color gradation with a value range between 0-255. 𝑔𝑟𝑎𝑦 = 0.2989 * 𝑅 + 0.587 * 𝐺 + 0.1141 * 𝐵…”

Section: Methodsmentioning

confidence: 99%

“…The canny operator is an optimal edge detector and produces smooth edges that are filtered using the Gaussian Derivative Kernel [31]. This method uses a Gaussian filter to smooth the image and reduce the edge detector's apparent noise effect (8).…”

Section: Methodsmentioning

confidence: 99%

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

Self Cite

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This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

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“…Resize is used to resize the resolution of the original image to the size that suits the needs of the method. Reshape is used to change back the image resolution size from the previous image size and modify the dimensions of the original matrix according to the needs of the method [12]. In this study using resize 224 and reshape 255.…”

Section: Pre Process Training and Testingmentioning

confidence: 99%

Date Palm Identification using DenseNet-201 Transfer Learning Method

Bin Raman,

Subekti

2023

j. nas. pendidik. teknik. inform.

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There are more than 400 types of dates in the world that are similar in size, shape, colour, fruit texture, taste and maturity, making it difficult for people to memorise them. Identification with artificial intelligence can make labelling dates easier. This research proposes the DenseNet-201 transfer learning method with freeze all the pre-trained layers, re-train all the pre-trained layers, and hyperparameter models for date variety identification. The date dataset was collected from the market with a total of 3,300 images of 11 types of dates, including Ajwa, Bam, Golden, Khalas, Khenaizi, Lulu, Mabroum, Medjool, Safawi, Sukari and Tunisian. The purpose of the research is to identify, analyse the test images and compare and recommend the best performance model to identify the type of dates. The experimental results have resulted in the recommendation that the DenseNet-201 method with the hyperparameter model shows the best performance with an accuracy value of 99.39%.

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Fish detection using morphological approach based-on k-means segmentation

Cited by 6 publications

References 26 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

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Date Palm Identification using DenseNet-201 Transfer Learning Method

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