Size Estimation for Shrimp Using Deep Learning Method

• 영문명: Size Estimation for Shrimp Using Deep Learning Method
• 발행기관: 한국스마트미디어학회
• 저자명: Heng Zhou Sung-Hoon Kim Sang-Cheol Kim Cheol-Won Kim Seung-Won Kang
• 간행물 정보: 『스마트미디어저널』Vol12, No.3, 112~119쪽, 전체 8쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2023.04.30

영문 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.

목차

Ⅰ. INTRODUCTION

Ⅱ. RELATED WORK

Ⅲ. PROPOSED METHOD

Ⅳ. EXPERIMENTAL RESULTS

Ⅴ. CONCLUSION

키워드

computer vision instance segmentation image processing AI application