Mechatronics applications to fish sorting Part 1: Fish size identification

Ibrahim, M. Yousef; Wang, J.

doi:10.1109/isie.2009.5215660

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…To improve these methods, previous studies measured the lengths [2][3][4][5] and areas [6][7] of fish in two-dimensional images generated by a computer vision system; fish selection and weight estimation were then conducted. Lee et al (2001) performed three-dimensional measurements and restructured a two-dimensional cross-section photography scan into a three-dimensional surface using charge coupled device (CCD) cameras and a multi-line laser [8][9].…”

Section: Introductionmentioning

confidence: 99%

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Lee¹,

Kim²,

Park³

et al. 2014

Journal of Electrical Engineering and Technology

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-Growth analysis and selection of sea cucumbers (Stichopus japonicas) is typically performed through length or weight measurements. However, because sea cucumbers continuously change shape depending on the external environment, weight measurement has been the preferred approach. Weight measurements require extensive time and labor, moreover it is often difficult to accurately weigh sea cucumbers because of their wet surface. The present study measured sea cucumber features, including the body length, width, and thickness, by using a vision system and regression analysis to generate R 2 values that were used to develop a weight estimation algorithm. The R 2 value between the actual volume and weight of the sea cucumbers was 0.999, which was relatively high. Evaluation of the performance of this algorithm using cross-validation showed that the root mean square error and worst-case prediction error were 1.434 g and ±5.879 g, respectively. In addition, the present study confirmed that the proposed weight estimation algorithm and single slide rail device for weight measurement can measure weights at approximately 4,500 sea cucumbers per hour.

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Section: Introductionmentioning

confidence: 99%

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Lee¹,

Kim²,

Park³

et al. 2014

Journal of Electrical Engineering and Technology

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“…Proposed algorithms from previous studies [13,14,17,18] have large measurement errors for morphometric characteristics of fish, because, these algorithms measure only the TL of the fish, and were mainly designed in such a way that the midpoints of the width of the fish at various positions along the length of the fish can be joined together to define a line which estimates the TL.…”

Section: Discussionmentioning

confidence: 99%

“…In the field of fisheries, there have been a few attempts at machine vision applications such as parasite detection [11], bone detection [12], and length measurement for fish sorting [13][14][15][16] or grading [17] by length. The vision-based measurement and sorting for fish are optimal methods without damaging the appearance of fish, and the methods can be measured the of morphometric characteristics of fish quickly.…”

Section: Introductionmentioning

confidence: 99%

“…In TL measurement of flatfish, the proposed measurement algorithm is simplified and improved than previous measurement algorithm [13][14][15][16][17][18]. Because, the proposed algorithm was designed in such a way that only three point such as point of the flatfish's jaw, CP and tail can be joined together to define a line which estimates the TL.…”

Section: Introductionmentioning

confidence: 99%

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Vision-based Automatic System for Non-contact Measurement of Morphometric Characteristics of Flatfish

Jeong¹,

Yang²,

Lee³

et al. 2013

Journal of Electrical Engineering and Technology

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-This paper introduces a vision-based automatic system (VAMS) for non-contact measurement of morphometric characteristics of flatfish, such as total length (TL), body width (BW), height (H), and weight (W). The H and W are simply measured by a laser displacement and a load cell, respectively. The TL and BW are measured by a proposed morphological image processing algorithm. The proposed algorithm cans measurement, when the tail of flatfish is deformed, and when it is randomly oriented. In the experiment, the average and maximum measurement errors were recorded, and standard deviations and coefficients of variation (CVs) for the measurements were calculated. From those results, when flatfish the TL measurements had an average of 266.844 mm, a standard deviation of 0.351 mm, a CV of 0.131%, and a maximum error of 0.87 mm with straightened flatfish (TL A : 267 mm, BW A : 141 mm), and when flatfish of different sizes were measured, the errors in the TL and BW measurements were both about 0.2 %. Using a single conveyor, the VAMS can process up to 900 fishes per hour. Moreover, it can measure morphometric characteristics of flatfish with a TL of up to 500 mm.

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“…With regard to length, most of these methods measure the link length between the fish width midpoints (Strachan ; Ibrahim and Wang, ; Jeong and others ). With regard to weight estimation, the methods use a regression formula between the projected two‐dimensional (2D) view area and the weight (Gümüş and Balaban, ; Balaban and others , ).…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimum Computer Vision‐Based Automatic Abalone (Haliotis discus hannai) Grading Algorithm

Lee

Kim

et al. 2015

Journal of Food Science

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An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively.

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Mechatronics applications to fish sorting Part 1: Fish size identification

Cited by 7 publications

References 11 publications

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Vision-based Automatic System for Non-contact Measurement of Morphometric Characteristics of Flatfish

Design of an Optimum Computer Vision‐Based Automatic Abalone (Haliotis discus hannai) Grading Algorithm

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