用两阶段框架提升流水线上苹果质量检测的稳定性。
A Two-Stage Detection-Tracking Framework for Stable Apple Quality Inspection in Dense Conveyor-Belt Environments
- 先用YOLOv8定位苹果，再用ByteTrack追踪身份
- 跟踪级聚合使缺陷判断更稳定，帧间波动减少
- 适合需要长时间连续检测的智能分拣系统
工业水果检测系统需在密集多物体交互和持续运动下可靠运行，但现有方法多在图像层面评估检测或分类，缺乏视频流中的时间稳定性。本文提出一种两阶段检测-追踪框架，用于流水线环境中稳定的多苹果质量检测。基于果园数据训练的YOLOv8模型完成苹果定位，随后通过ByteTrack实现多目标追踪以维持身份持久性。一个在健康-缺陷果实数据集上微调的ResNet18缺陷分类器作用于裁剪出的苹果区域。引入跟踪级聚合机制，强化时间一致性并降低帧间预测波动。定义了如跟踪级缺陷率、时间一致性等视频级工业指标，用于评估系统在真实工况下的鲁棒性。实验表明，相比逐帧推理，该方法显著提升了稳定性，证明集成追踪对实际自动化水果分级系统至关重要。
原文摘要 · Abstract (English)
Industrial fruit inspection systems must operate reliably under dense multi-object interactions and continuous motion, yet most existing works evaluate detection or classification at the image level without ensuring temporal stability in video streams. We present a two-stage detection-tracking framework for stable multi-apple quality inspection in conveyor-belt environments. An orchard-trained YOLOv8 model performs apple localization, followed by ByteTrack multi-object tracking to maintain persistent identities. A ResNet18 defect classifier, fine-tuned on a healthy-defective fruit dataset, is applied to cropped apple regions. Track-level aggregation is introduced to enforce temporal consistency and reduce prediction oscillation across frames. We define video-level industrial metrics such as track-level defect ratio and temporal consistency to evaluate system robustness under realistic processing conditions. Results demonstrate improved stability compared to frame-wise inference, suggesting that integrating tracking is essential for practical automated fruit grading systems.
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