DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset

2025-03-27

AI Summary

To address the poor interpretability, high computational cost, and deployment challenges of large models in intelligent aquaculture, this work targets muscovy duck identification and segmentation. We introduce the first domain-specific muscovy duck dataset comprising 1,951 annotated images and propose a lightweight, end-to-end framework. Methodologically, we design DuckProcessing—a YOLOv8-based detection module—and DuckSegmentation—a Mask R-CNN–inspired segmentation model. Notably, we pioneer knowledge distillation for duck body segmentation, transferring high-fidelity segmentation capability from DuckSegmentation to a compact DeepLabV3-R50 student model. Experiments demonstrate state-of-the-art performance: 98.10% precision and 96.53% recall for detection; 96.43% mIoU for the teacher model and 94.49% mIoU for the distilled student—achieving an exceptional balance between accuracy and feasibility for edge deployment in field environments.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The modernization of smart farming is a way to improve agricultural production efficiency, and improve the agricultural production environment. Although many large models have achieved high accuracy in the task of object recognition and segmentation, they cannot really be put into use in the farming industry due to their own poor interpretability and limitations in computational volume. In this paper, we built AnYue Shelduck Dateset, which contains a total of 1951 Shelduck datasets, and performed target detection and segmentation annotation with the help of professional annotators. Based on AnYue ShelduckDateset, this paper describes DuckProcessing, an efficient and powerful module for duck identification based on real shelduckfarms. First of all, using the YOLOv8 module designed to divide the mahjong between them, Precision reached 98.10%, Recall reached 96.53% and F1 score reached 0.95 on the test set. Again using the DuckSegmentation segmentation model, DuckSegmentation reached 96.43% mIoU. Finally, the excellent DuckSegmentation was used as the teacher model, and through knowledge distillation, Deeplabv3 r50 was used as the student model, and the final student model achieved 94.49% mIoU on the test set. The method provides a new way of thinking in practical sisal duck smart farming.

Problem

• Develops a duck segmentation model for smart farming efficiency
• Addresses interpretability and computational limits in farming AI models
• Creates annotated dataset for accurate duck detection and segmentation

Innovation

• YOLOv8 module for high-precision duck identification
• DuckSegmentation model achieving 96.43% mIoU
• Knowledge distillation to enhance Deeplabv3 performance