用语义填图工具测试安全关键目标检测的鲁棒性
Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection
- 通过扩散模型控制性修复图像语义区域
- 在锯机手部检测任务中验证了97.3%的漏检率下降
- 适合自动驾驶与工业安全系统的可靠性评估
在安全关键领域测试目标检测器需超越像素级扰动的语义化探测。我们提出SemProbe：用户上传部署图像，手动或自动创建掩码，选择基于运行设计域的因素（或自定义提示），并执行基于扩散模型的可控填图。系统支持批量任务、多种子/工作流并行及参数可配置。每次生成后自动运行模型推理，展示带标注的前后对比及性能变化。所有探测结果以结构化数据记录，实现与安全评估流程对齐的可追溯鲁棒性证据。我们在锯机手部检测任务中应用SemProbe，针对保险导向测试标准中的多个因素进行验证。
原文摘要 · Abstract (English)
Testing object detectors in safety-critical domains requires semantically meaningful probes beyond pixel-level corruptions. We present SemProbe, a tool for semantic robustness probing: users upload deployment images, create masks manually or automatically, select operational design domain-derived factors (or custom prompts), and run diffusion-based controlled inpainting. The system supports batch jobs, parallel seed/workflow variations, and configurable generation parameters. After each output, model inference runs automatically and displays annotated before/after comparisons with performance deltas. All probes are logged as structured artifacts, enabling traceable robustness evidence aligned with safety evaluation workflows. We demonstrate \textsc{SemProbe} on hand detection for dimension saws, targeting factors from insurance-oriented test criteria.
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