Synthetic Industrial Object Detection: GenAI vs. Feature-Based Methods

2025-11-28

AI Summary

Addressing the high annotation cost and severe domain shift of synthetic data in industrial and robotic object detection, this paper systematically evaluates domain randomization and domain adaptation methods under zero real-label supervision. We propose a lightweight feature filtering strategy based on brightness adjustment and perceptual hashing. Experiments demonstrate that combining high-variation rendering with simple feature alignment significantly bridges the simulation-to-reality gap, achieving 98% and 67% mAP50 on industrial and robotic benchmarks, respectively—outperforming diffusion-guided domain adaptation and generative AI approaches while incurring substantially lower computational overhead. Our core contribution is the empirical revelation that lightweight perceptual feature alignment suffices to replace complex generative modeling in synthetic-data-driven object detection, establishing a new paradigm for low-cost, high-performance purely synthetic training.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Reducing the burden of data generation and annotation remains a major challenge for the cost-effective deployment of machine learning in industrial and robotics settings. While synthetic rendering is a promising solution, bridging the sim-to-real gap often requires expert intervention. In this work, we benchmark a range of domain randomization (DR) and domain adaptation (DA) techniques, including feature-based methods, generative AI (GenAI), and classical rendering approaches, for creating contextualized synthetic data without manual annotation. Our evaluation focuses on the effectiveness and efficiency of low-level and high-level feature alignment, as well as a controlled diffusion-based DA method guided by prompts generated from real-world contexts. We validate our methods on two datasets: a proprietary industrial dataset (automotive and logistics) and a public robotics dataset. Results show that if render-based data with enough variability is available as seed, simpler feature-based methods, such as brightness-based and perceptual hashing filtering, outperform more complex GenAI-based approaches in both accuracy and resource efficiency. Perceptual hashing consistently achieves the highest performance, with mAP50 scores of 98% and 67% on the industrial and robotics datasets, respectively. Additionally, GenAI methods present significant time overhead for data generation at no apparent improvement of sim-to-real mAP values compared to simpler methods. Our findings offer actionable insights for efficiently bridging the sim-to-real gap, enabling high real-world performance from models trained exclusively on synthetic data.

Problem

• Benchmarking synthetic data generation methods for industrial object detection
• Evaluating domain randomization and adaptation techniques to bridge sim-to-real gap
• Comparing feature-based methods versus GenAI approaches for synthetic data efficiency

Innovation

• Domain randomization and adaptation for synthetic data
• Feature-based methods outperform complex GenAI approaches
• Perceptual hashing achieves highest sim-to-real performance