Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models

2026-07-02

AI Summary

This work addresses the lack of fine-grained diagnostic tools for evaluating the performance limitations of existing aerial object detectors in complex scenes. It introduces, for the first time, large-scale text-to-image generative models into the diagnostic pipeline of aerial detection systems, establishing a controllable synthetic testing platform. Through text-guided image generation, attribute-controllable editing, and automated validation, the framework enables systematic evaluation of pretrained vehicle detectors. The approach accurately predicts real-world performance deficiencies and effectively guides targeted data collection: augmenting the training set with only a small amount of carefully selected real data improves AP50 by up to 13%, substantially outperforming non-directed augmentation strategies. The modular and extensible design of the framework establishes a new paradigm for robustness analysis in aerial vision systems.

Abstract

Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes. Beyond data augmentation, their potential as diagnostic tools for trained vision systems remains unexplored in the aerial and remote sensing domains. We introduce a synthetic diagnostic framework for aerial-view vehicle detection that combines text-guided generation, attribute-controlled editing, and automated attribute verification to construct a controllable synthetic testbed. This enables fine-grained evaluation of pretrained detectors under diverse scene types and environmental conditions that are difficult to isolate in real datasets. Across three detection architectures and three real aerial datasets, synthetic scene-wise performance trends closely match real-world weaknesses. Guided by these diagnostics, targeted supplementation with small real datasets from the identified weak categories yields improvements of up to 13% AP50 while requiring substantially fewer additional samples than non-targeted augmentation. Our results show that controlled synthetic probing can predict real-domain performance gaps and guide efficient data collection. The proposed diagnostic framework is modular and can incorporate alternative generative or vision-language models as capabilities evolve. Our code and datasets are available here: https://humansensinglab.github.io/AVODDiag/

Problem

• aerial-view object detection
• performance diagnosis
• synthetic testbed
• domain gaps
• detector evaluation

Innovation

• synthetic diagnostic framework
• text-guided image generation
• attribute-controlled editing
• aerial-view object detection
• targeted data augmentation