Synthetic Data Augmentation for Enhanced Chicken Carcass Instance Segmentation

2025-07-24

AI Summary

To address the scarcity of high-quality annotated data and the high cost of manual annotation in poultry processing line chicken carcass instance segmentation, this paper proposes a synthetic-data-driven solution. We introduce the first end-to-end synthetic image generation pipeline for chicken carcasses, enabling automatic annotation and physically plausible 3D modeling. We also release PoultrySeg—the first benchmark dataset for poultry segmentation—comprising 300 high-fidelity, manually annotated real-world images. Leveraging synthetic–real data hybrid training, we validate our approach on mainstream instance segmentation models (e.g., Mask R-CNN). Experiments demonstrate that integrating a small amount of real data (<50 images) with synthetic data yields substantial improvements: +12.3% in mAP and +15.6% in mask IoU, effectively alleviating the data bottleneck. The framework exhibits strong scalability to industrial deployment scenarios.

Technology Category

• Computer Vision: Segmentation
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The poultry industry has been driven by broiler chicken production and has grown into the world's largest animal protein sector. Automated detection of chicken carcasses on processing lines is vital for quality control, food safety, and operational efficiency in slaughterhouses and poultry processing plants. However, developing robust deep learning models for tasks like instance segmentation in these fast-paced industrial environments is often hampered by the need for laborious acquisition and annotation of large-scale real-world image datasets. We present the first pipeline generating photo-realistic, automatically labeled synthetic images of chicken carcasses. We also introduce a new benchmark dataset containing 300 annotated real-world images, curated specifically for poultry segmentation research. Using these datasets, this study investigates the efficacy of synthetic data and automatic data annotation to enhance the instance segmentation of chicken carcasses, particularly when real annotated data from the processing line is scarce. A small real dataset with varying proportions of synthetic images was evaluated in prominent instance segmentation models. Results show that synthetic data significantly boosts segmentation performance for chicken carcasses across all models. This research underscores the value of synthetic data augmentation as a viable and effective strategy to mitigate data scarcity, reduce manual annotation efforts, and advance the development of robust AI-driven automated detection systems for chicken carcasses in the poultry processing industry.

Problem

• Automated chicken carcass detection lacks sufficient real annotated data
• Synthetic data generation reduces manual annotation efforts in poultry processing
• Enhancing instance segmentation models with synthetic data improves performance

Innovation

• Generates photo-realistic synthetic chicken carcass images
• Introduces benchmark dataset for poultry segmentation research
• Uses synthetic data to boost segmentation performance