提出鲁棒视觉系统，实现脏污锈蚀环境下螺钉精准检测与拆卸。
Industrial-Grade Robust Robot Vision for Screw Detection and Removal under Uneven Conditions
- 两阶段检测+网格化局部标定，适应尺寸差异与恶劣环境。
- 螺钉检测召回率达99.8%，拆卸精度达±0.75毫米。
- 工业实测成功率78.3%，适合回收厂自动化产线部署。
随着日本废旧家电数量上升而劳动力减少，回收厂需自动化拆解流程。空调外机拆解因尺寸差异和脏污锈蚀仍具挑战。本文提出集成任务专用两阶段检测与基于网格的局部标定策略的自动化系统，在严重退化条件下实现99.8%的螺钉检测召回率，并在无预设坐标情况下保证±0.75 mm的操作精度。在120台真实外机上验证，系统拆解成功率达78.3%，平均周期为193秒，证实其工业应用可行性。
原文摘要 · Abstract (English)
As the amount of used home appliances is expected to increase despite the decreasing labor force in Japan, there is a need to automate disassembling processes at recycling plants. The automation of disassembling air conditioner outdoor units, however, remains a challenge due to unit size variations and exposure to dirt and rust. To address these challenges, this study proposes an automated system that integrates a task-specific two-stage detection method and a lattice-based local calibration strategy. This approach achieved a screw detection recall of 99.8% despite severe degradation and ensured a manipulation accuracy of +/-0.75 mm without pre-programmed coordinates. In real-world validation with 120 units, the system attained a disassembly success rate of 78.3% and an average cycle time of 193 seconds, confirming its feasibility for industrial application.
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