Baby Shrimp Counting via Automated Image Processing

Kesvarakul, Ramil; Chianrabutra, Chamaporn; Chianrabutra, Srisit

doi:10.1145/3055635.3056652

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…However, this approach is only feasible in underwater settings and not well suited for intricate backgrounds. Kesvarakul [11] put forward a method for shrimp larva counting using image detection for spot information, which can reduce errors by 6.9% compared to traditional manual counting. However, this method requires comparison of the transparent parts of the shrimp larvae with the surrounding water environment, and the accuracy of counting may be interfered with by water impurities and changes in appearance, and parameters need to be adjusted as the aquaculture environment changes.…”

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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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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“…With the application of computer vision technology in aquaculture, target detection algorithms are adopted to solve the problem of accurate counting by acquiring biological image data through HD cameras and other devices. Traditional target detection algorithms use image processing methods to extract features [3][4][5][6]. Zhang [7] applied image processing methods such as binarization, expansion and erosion to extract fry images and employed connected area algorithm and refinement algorithm to count bass in the images.…”

Section: Introductionmentioning

confidence: 99%

Bass detection model based on improved YOLOv5 in circulating water system

Deng

Cao

et al. 2023

PLoS ONE

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The feeding amount of bass farming is closely related to the number of bass. It is of great significance to master the number of bass to achieve accurate feeding and improve the economic benefits of the farm. In view of the interference caused by the problems of multiple targets and target occlusion in bass data for bass detection, this paper proposes a bass target detection model based on improved YOLOV5 in circulating water system. Firstly, acquiring by HD cameras, Mosaic-8, a data augmentation method, is utilized to expand datasets and improve the generalization ability of the model. And K-means clustering algorithm is applied to generate suitable coordinates of prior boxes to improve training efficiency. Secondly, Coordinate Attention mechanism (CA) is introduced into backbone feature extraction network and neck feature fusion network to enhance attention to targets of interest. Finally, Soft-NMS algorithm replaces Non-Maximum Suppression algorithm (NMS) to re-screen prediction boxes and keep targets with higher overlap, which effectively solves the problems of missed detection and false detection. The experiments show that the proposed model can reach 98.09% in detection accuracy and detection speed reaches 13.4ms. The proposed model can help bass farmers under the circulating water system to accurately grasp the number of bass, which has important application value to realize accurate feeding and water conservation.

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“…Therefore, shrimp larvae cannot be weighed for estimation, and the quantity must be used as a proxy for the value of the larvae. Nevertheless, the small size of the larvae and their large numbers in transactions make manual counting a labor-intensive process, taking an average of 15–20 min to count 500–700 shrimp larvae [ 4 ]. There is an imminent need for an accurate and efficient method for counting shrimp larvae.…”

Section: Introductionmentioning

confidence: 99%

Automatic Penaeus Monodon Larvae Counting via Equal Keypoint Regression with Smartphones

Liu

Wang

et al. 2023

Animals

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Today, large-scale Penaeus monodon farms no longer incubate eggs but instead purchase larvae from large-scale hatcheries for rearing. The accurate counting of tens of thousands of larvae in these transactions is a challenging task due to the small size of the larvae and the highly congested scenes. To address this issue, we present the Penaeus Larvae Counting Strategy (PLCS), a simple and efficient method for counting Penaeus monodon larvae that only requires a smartphone to capture images without the need for any additional equipment. Our approach treats two different types of keypoints as equip keypoints based on keypoint regression to determine the number of shrimp larvae in the image. We constructed a high-resolution image dataset named Penaeus_1k using images captured by five smartphones. This dataset contains 1420 images of Penaeus monodon larvae and includes general annotations for three keypoints, making it suitable for density map counting, keypoint regression, and other methods. The effectiveness of the proposed method was evaluated on a real Penaeus monodon larvae dataset. The average accuracy of 720 images with seven different density groups in the test dataset was 93.79%, outperforming the classical density map algorithm and demonstrating the efficacy of the PLCS.

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Baby Shrimp Counting via Automated Image Processing

Cited by 12 publications

References 4 publications

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

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

Bass detection model based on improved YOLOv5 in circulating water system

Automatic Penaeus Monodon Larvae Counting via Equal Keypoint Regression with Smartphones

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