中文标题#
鱼眼目标检测的边缘案例合成：一种数据驱动的视角
英文标题#
Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective
中文摘要#
鱼眼相机引入了显著的失真，并对在传统数据集上训练的目标检测模型提出了独特的挑战。 在本工作中，我们提出了一种以数据为中心的流程，通过专注于识别模型的盲点这一关键问题，系统地提高检测性能。 通过详细的错误分析，我们识别出关键的边缘案例，如混淆的类别对、外围失真和未充分表示的上下文。 然后我们通过边缘案例合成直接解决这些问题。 我们微调了一个图像生成模型，并通过精心设计的提示来引导它，生成能够复制现实世界失败模式的图像。 这些合成图像使用高质量的检测器进行伪标记，并整合到训练中。 我们的方法带来了稳定的性能提升，突显了在像鱼眼目标检测这样的专业领域中，深入理解数据并有针对性地修复其弱点可以产生巨大的影响。
英文摘要#
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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