外文摘要：Pre-treatment processing technology plays a crucial role in the overall freshwater fish processing procedure, and automatic head and tail cutting stands out as a significant pre-treatment technique within the industry. The system for removing the head and tail of freshwater fish comprised a Cartesian coordinate manipulator, a fish transfer device, a control system, and an image acquisition device. In the vision system, five image segmentation methods were utilized for fish head and tail image segmentation comparison tests. These methods include U-Net (U-shaped Deep Neural Network), DeeplabV3, PSPNet (Pyramid Scene Parsing Network), FastSCNN (Fast Semantic Segmentation Network), and ICNet (Image Cascade Network), all of which were employed to evaluate their performance. Among the tested segmentation methods, the ICNet demonstrated the most excellent segmentation capability. The experimental results indicated a segmentation accuracy of 99.01%, a mean intersection over union (MIoU) of 82.50%, and an image processing time of 15.25 ms. The results showed that the fish head and tail were successfully cut off using this model for recognition with a circular knife. Consequently, the segmentation model employed in the machine vision system within this study has demonstrated successful applicability in automatically cutting the heads and tails of freshwater fish of various sizes.
外文关键词：image segmentation;Manipulator;freshwater fish;head and tail;ICNet
作者：Hu, Zhigang;Jiang, Yajun;Fu, Dandan;Chen, Yan;Peng, Xianhui
作者单位：Wuhan Polytech Univ
期刊名称：INTERNATIONAL JOURNAL OF FOOD PROPERTIES
期刊影响因子：0.0
出版年份：2024
出版刊次：27（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台