Automatic segmentation method for live fish eggs microscopic image analysis

Duan, Yane; Li, Bingbing; Stien, Lars Helge; Fu, Zetian; Wright, Daniel; Ya, Gao

doi:10.1016/j.aquaeng.2019.01.004

Cited by 14 publications

(3 citation statements)

References 15 publications

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“…However, fish egg counts in Petri dishes are not accurate, which is affected by specular reflections and still faces challenges of low contrast because of the high transparency of fish eggs. Furthermore, Duan et al (2019) utilized a series of automatic segmentation method to count fish eggs in the microscopic image by a series of algorithms, including Ostu's method, morphological operations, and Watershed algorithm.…”

Section: Segmentation-based Methodsmentioning

confidence: 99%

Automatic counting methods in aquaculture: A review

Miao

Peng

et al. 2020

J World Aquaculture Soc

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Object counting in aquaculture is an important task, and has been widely applied in fish population estimation, estimation of lobster abundance and scallop stocks, and so forth. However, underwater object counting is challenging for biologists and marine scientists because of the diversity of backgrounds of the lake or ocean, the uncertainty of the object motion, and the occlusion between objects. With the rapid development of sensor, computer vision, and acoustic technologies, advanced and efficient counting methods are available in aquaculture. We reviewed underwater object counting methods in aquaculture, provided a survey including more than 50 articles in the recent 10 years, and analyzed the pros and cons of the counting methods and the applicable scenarios of those methods. Finally, the major challenges and future trends of underwater object counting in aquaculture are discussed.

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Section: Segmentation-based Methodsmentioning

confidence: 99%

Automatic counting methods in aquaculture: A review

Miao

Peng

et al. 2020

J World Aquaculture Soc

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“…In image segmentation-based counting methods, target adhesion is an important factor affecting the counting accuracy, and for the problem of the presence of adhesions among the fish, Labuguen et al [4] adopted adaptive thresholding segmentation and edge detection to segment and count fry. Duan et al [24] applied morphological operations to double layer images, along with a watershed algorithm based on the Otsu automatic threshold segmentation algorithm, and achieved good results in segmenting and counting fish eggs.…”

Section: Introductionmentioning

confidence: 99%

Fry Counting Models Based on Attention Mechanism and YOLOv4-Tiny

et al. 2022

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Accurate counting is difficult in the case of large numbers of overlapping and adhering fry. In this study, we propose a lightweight target detection counting method based on deep learning methods that can meet the deployment requirements of edge computing device for automatic fry counting while obtaining a high counting accuracy. We improve the structure of YOLOv4-tiny by embedding different attention mechanisms in the CSP blocks of the backbone network to enhance the feature extraction performance. In addition, the low efficiency of feature fusion in the original model is also addressed by adding different attention mechanisms to the neck network structure to promote the effective fusion of deep feature information with shallow feature information and improve the counting accuracy. The experimental results showed that the six models proposed in this study improved the model accuracy and recall to varying degrees compared with the original YOLOv4-tiny model, while retaining the advantages of the YOLOv4-tiny model in terms of its small number of parameters and fast inference rate. It was also shown that the CBAM(n)-YOLOv4-tiny model obtained by adding the CBAM to the neck network showed the most significant improvement, with an mean average precision (mAP) of 94.45% and a recall of 93.93%.

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“…The author discussed a method for segmenting overlapping objects that outperforms two state-of-the-art approaches currently available. Yane Duan et al [3] demonstrated an algorithm for segmenting microscopic images of live fish eggs automatically. The proposed method accurately recognized, segmented, and counted objects in all 96 test cases when compared to manual observation.…”

Section: Introductionmentioning

confidence: 99%

Classification of Overlapping Eggs Based on Image Processing

Purahong

Krungseanmuang

Chaowalittawin

et al. 2022

J. Phys.: Conf. Ser.

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This paper presents a method for classifying the overlapped eggs and counting the number of eggs on the conveyor belt using image processing techniques. The image was acquired by a webcam camera that connected to the computer and then rescaled. The image was then converted to grayscale and noise was reduced using a Gaussian blur filter. Otsu’s Binarization is used to convert the image to binary. The binary image is then subjected to morphological operations. Following that, using the Watershed Algorithm, separate the egg’s overlapped area. Finally, the prepared image is ready to be counted using the contour matrix method. This method independently classifies each egg segmentation and can count up to 18 eggs per frame with a processing time of less than 1 second.

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Automatic segmentation method for live fish eggs microscopic image analysis

Cited by 14 publications

References 15 publications

Automatic counting methods in aquaculture: A review

Automatic counting methods in aquaculture: A review

Fry Counting Models Based on Attention Mechanism and YOLOv4-Tiny

Classification of Overlapping Eggs Based on Image Processing

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