针对鱼眼相机检测难题，通过合成边缘案例提升模型性能。
Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective
- 分析模型盲区，针对性生成真实失效场景的合成图像。
- 在多个鱼眼数据集上实现检测精度显著提升。
- 适合关注小众视觉任务与数据增强的开发者。
鱼眼相机引入显著畸变，使传统数据集训练的检测模型面临挑战。本文提出一种以数据为中心的流程，通过细致误差分析识别关键边缘案例，如类别混淆、边缘畸变和低频上下文。随后，利用微调的图像生成模型，结合精心设计的提示词，生成复现真实失败模式的合成图像。这些合成图像通过高质量检测器进行伪标注，并融入训练过程。实验表明该方法带来持续的性能提升，凸显了深入理解数据并针对性修复其弱点在鱼眼目标检测等特殊领域的重要价值。
原文摘要 · Abstract (English)
Fisheye cameras introduce significant distortion and pose unique challenges to object detection models trained on conventional datasets. In this work, we propose a data-centric pipeline that systematically improves detection performance by focusing on the key question of identifying the blind spots of the model. Through detailed error analysis, we identify critical edge-cases such as confusing class pairs, peripheral distortions, and underrepresented contexts. Then we directly address them through edge-case synthesis. We fine-tuned an image generative model and guided it with carefully crafted prompts to produce images that replicate real-world failure modes. These synthetic images are pseudo-labeled using a high-quality detector and integrated into training. Our approach results in consistent performance gains, highlighting how deeply understanding data and selectively fixing its weaknesses can be impactful in specialized domains like fisheye object detection.
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