通过图像增强与背景去除提升工业场景下物体检测精度
BioDet: Boosting Industrial Object Detection with Image Preprocessing Strategies
- 用低光增强和基于大模型的背景移除降低域偏移
- 在BOP真实工业数据集上检测准确率显著提升
- 适合需要高精度姿态估计的机器人抓取场景
精确的6D姿态估计对工业环境中的机器人操作至关重要。现有流程通常依赖现成的目标检测器，经裁剪后进行姿态精修，但在杂乱、光照差、背景复杂等条件下性能下降，检测成为主要瓶颈。本文提出一种标准化、可插拔的2D检测流水线，用于工业场景中未见物体的检测。基于当前SOTA基线，通过低光图像增强与基于开放词汇检测的背景移除策略，缓解领域偏移和背景干扰。该设计有效抑制了原始SAM输出中的误检，为下游姿态估计提供更可靠的检测结果。在来自BOP的真实工业料箱抓取基准上的大量实验表明，本方法显著提升了检测精度，且推理开销可忽略不计，验证了其有效性与实用性。
原文摘要 · Abstract (English)
Accurate 6D pose estimation is essential for robotic manipulation in industrial environments. Existing pipelines typically rely on off-the-shelf object detectors followed by cropping and pose refinement, but their performance degrades under challenging conditions such as clutter, poor lighting, and complex backgrounds, making detection the critical bottleneck. In this work, we introduce a standardized and plug-in pipeline for 2D detection of unseen objects in industrial settings. Based on current SOTA baselines, our approach reduces domain shift and background artifacts through low-light image enhancement and background removal guided by open-vocabulary detection with foundation models. This design suppresses the false positives prevalent in raw SAM outputs, yielding more reliable detections for downstream pose estimation. Extensive experiments on real-world industrial bin-picking benchmarks from BOP demonstrate that our method significantly boosts detection accuracy while incurring negligible inference overhead, showing the effectiveness and practicality of the proposed method.
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