Method of famous tea sprout identification and segmentation based on improved watershed algorithm

Zhang, Lei; Zou, Lang; Wu, Chuanyu; Jia, Jiangming; Chen, Jianneng

doi:10.1016/j.compag.2021.106108

Cited by 40 publications

(33 citation statements)

References 19 publications

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“…Then, the edge histogram texture descriptor (CLAHE and Sobel edge detector [32]) was applied to analyze and detect structures. The Watershed algorithm [31] was applied to extract contour shape and localize the disc part on the OD. A cropping method was used to Curson the image to the required size.…”

Section: Review Of Methodsmentioning

confidence: 99%

Segmentation and Classification of Retinal Fundus Images using Convolutional Neural Networks: A systematic review of methods and latest trends

Ilesanmi

Idowu

et al. 2022

Preprint

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The retina fundus images are critical diagnostic tools for the early detection of ophthalmic problems. Prompt diagnosis and treatment of eye-related problems are important to prevent blindness or vision loss. Recently, CNN algorithms have become effective in tasks relating to recognition, delineation, and classification. Therefore we propose a review to summarize different CNN algorithms for segmenting and classifying retinal fundus images. This review systematically searches different repositories for methods that use CNN for the segmentation and classification of retinal fundus images. A thorough screening of abstracts and titles to ascertain their relevance was done. A total of fifty-two studies were included in our review, with content such as database usage, disadvantages, and advantages. A comparison of two accuracies was also carried out with a graph depicting database usage. Important insights, limitations, observations, and future directions were elucidated. Finally, findings suggest that CNN algorithms produce good accuracies despite their limitations.

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Section: Review Of Methodsmentioning

confidence: 99%

Segmentation and Classification of Retinal Fundus Images using Convolutional Neural Networks: A systematic review of methods and latest trends

Ilesanmi

Idowu

et al. 2022

Preprint

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“…This algorithm is a transformation defined on a grayscale image. This transformation treats the image as a topographic map, with the brightness of each point representing its height, and finds the lines that run along the tops of ridges [26][27][28][29]. In this segmentation, we adopt this algorithm to obtain a continuous and close edge of each protection platen.…”

Section: ) Segmentation and Feature Extraction Using The Proposed W-c...mentioning

confidence: 99%

Automatic State Recognition of Multi-type Protection Platens based on YOLOv5 and Parallel Multi-Decision Algorithm

Zhang¹,

Zhang²,

Wu³

et al. 2022

IJACSA

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A protection platen is a vital component in relay protection systems. The manual inspection of protection platen states is long-term repetitive work with low efficiency and imposes a heavy burden on workers. In this work, we propose a new system to automatically detect the states of multi-type protection platens in images. This system can classify two protection platen categories and further recognize the states of protection platens. For the classification of protection platen types, we propose a new algorithm that automatically detects two protection platen types based on HSV (Hue, Saturation, Value) color space weighting operators. The proposed operators quantify the color variation in the protection platen and reduce the influence of environmental factors. With respect to the state recognition of protection platens, the Type-I protection platen states are automatically classified by the YOLOv5 (You only look once version 5) network. Since the Type-II protection platen has three primary states and more complicated structures, we investigate a new parallel multi-decision algorithm to recognize the states of Type-II protection platens based on the newly proposed watershed-color space difference-shape feature (W-CD-SF) method and the YOLOv5 network. The W-CD-SF technique can segment the protection platens and extract their shape features automatically. This multi-decision mechanism improves the robustness and generalization of state recognition. Experiments were conducted on the collected protection platen images containing 8,969 protection platens. The recognition accuracies of protection platen states exceed 95%. This system can provide auxiliary detection and long-term monitoring of protection platen states.

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“…In this article, RGB and NIR images ( Kusumaningrum et al, 2018 ) collected by a multispectral camera were used to train a CNN model. To solve the problem of corn seed adhesion and seed location during the recognition process, a watershed algorithm ( Lei et al, 2019 ; Sta et al, 2019 ; Zhang et al, 2021 ) combined with a two-way CNN ( Zhang J. J. et al, 2020 ) was proposed to detect corn seed defects. The results revealed that this method is with high accuracy, and the targets can be accurately located and classified.…”

Section: Introductionmentioning

confidence: 99%

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

et al. 2022

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Corn seed materials of different quality were imaged, and a method for defect detection was developed based on a watershed algorithm combined with a two-pathway convolutional neural network (CNN) model. In this study, RGB and near-infrared (NIR) images were acquired with a multispectral camera to train the model, which was proved to be effective in identifying defective seeds and defect-free seeds, with an averaged accuracy of 95.63%, an averaged recall rate of 95.29%, and an F1 (harmonic average evaluation) of 95.46%. Our proposed method was superior to the traditional method that employs a one-pathway CNN with 3-channel RGB images. At the same time, the influence of different parameter settings on the model training was studied. Finally, the application of the object detection method in corn seed defect detection, which may provide an effective tool for high-throughput quality control of corn seeds, was discussed.

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Method of famous tea sprout identification and segmentation based on improved watershed algorithm

Cited by 40 publications

References 19 publications

Segmentation and Classification of Retinal Fundus Images using Convolutional Neural Networks: A systematic review of methods and latest trends

Segmentation and Classification of Retinal Fundus Images using Convolutional Neural Networks: A systematic review of methods and latest trends

Automatic State Recognition of Multi-type Protection Platens based on YOLOv5 and Parallel Multi-Decision Algorithm

Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

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