Size Estimation for Shrimp Using Deep Learning Method
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초록
Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.
Abstract
Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.
참고문헌 (10)
- Seong, Hoon Je, "A case study on the distribution of the environmental resistome in Korean shrimp farms", Ecotoxicology and Environmental Safety, 227, pp.112858~ (2021)[DOI]
- Woo-Taeg Kwon, "Effect of Improving Quality by Changing the Distribution Method of Shrimp Culture", 유통과학연구, 19(4), pp.53~60 (2021), 한국유통과학회
- Roy, Luke A., "Effect of feeding rate and pond primary productivity on growth of Litopenaeus vannamei reared in inland saline waters of West Alabama", North American Journal of Aquaculture, 74(1), pp.20~26 (2012)[DOI]
- He, Kaiming, "Mask r-cnn", Proceedings of the IEEE international conference on computer vision, pp.2961~2969 (2017)
- Harbitz, Alf, "Estimation of shrimp (Pandalus borealis) carapace length by image analysis", ICES Journal of Marine Science, 64(5), pp.939~944 (2007)[DOI]
- Zhou, Heng, "Estimation of shrimp’s size based on 3D instance segmentation using deep learning technology", Proceedings of the Korean Conference on Control and Robot System, pp.311~312
- Zhang, Tongjie Y., "A fast parallel algorithm for thinning digital patterns", Communications of the ACM, 27(3), pp.236~239 (1984)[DOI]
- "Detectron2"[URL]
- Lin, Tsung-Yi, "Microsoft coco: Common objects in context", European conference on computer vision, pp.740~755 (2014), Springer
- Everingham, Mark, "The pascal visual object classes (voc) challenge", International journal of computer vision, 88(2), pp.303~338 (2010)[DOI]