用深度度量学习识别产线装配进度，能处理零件遮挡问题。
Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work
- 基于三元组损失加入异常样本，提升遮挡下的进度识别能力
- 在遮挡场景下达到82.9%的装配进度识别准确率
- 适合工业产线可视化监控，对遮挡鲁棒性强
本文提出一种考虑遮挡的深度度量学习进度识别方法，用于工厂产线装配过程的可视化。首先使用基于深度学习的目标检测方法从固定摄像头拍摄的图像中定位装配产品；随后裁剪检测区域；最后基于裁剪图像，采用深度度量学习分类方法估计装配进度为粗粒度步骤。针对具体模型，提出异常三元组网络（Anomaly Triplet-Net），通过在三元组损失中引入异常样本以增强对遮挡情况的适应性。实验表明，该方法在进度识别任务上达到82.9%的成功率。同时验证了检测-裁剪-估计流程的实际可行性，整体系统有效。
原文摘要 · Abstract (English)
In this paper, a progress recognition method consider occlusion using deep metric learning is proposed to visualize the product assembly process in a factory. First, the target assembly product is detected from images acquired from a fixed-point camera installed in the factory using a deep learning-based object detection method. Next, the detection area is cropped from the image. Finally, by using a classification method based on deep metric learning on the cropped image, the progress of the product assembly work is estimated as a rough progress step. As a specific progress estimation model, we propose an Anomaly Triplet-Net that adds anomaly samples to Triplet Loss for progress estimation considering occlusion. In experiments, an 82.9% success rate is achieved for the progress estimation method using Anomaly Triplet-Net. We also experimented with the practicality of the sequence of detection, cropping, and progression estimation, and confirmed the effectiveness of the overall system.
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