BioDet: Boosting Industrial Object Detection with Image Preprocessing Strategies
This work addresses the critical bottleneck of object detection for robotic manipulation in industrial settings, though it appears incremental as it builds on existing SOTA baselines with preprocessing enhancements.
The paper tackles the problem of 2D object detection for unseen objects in industrial environments, where performance degrades under challenging conditions like clutter and poor lighting. Their approach reduces domain shift and background artifacts using low-light enhancement and background removal, achieving significant detection accuracy improvements on real-world benchmarks with negligible inference overhead.
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.