Fully Automated Annotation With Noise-Masked Visual Markers for Deep-Learning-Based Object Detection

Automated factories use deep-learning-based vision systems to accurately detect various products. However, training such vision systems requires manual annotation of a significant amount of data to optimize the large number of parameters of the deep convolutional neural networks. Such manual annotation is very time-consuming and laborious. To reduce this burden, we propose a fully automated annotation approach without any manual intervention. To do this, we associate one visual marker with one object and capture them in the same image. However, if an image showing the marker is used for training, normally, the neural network learns the marker as a feature of the object. By hiding the marker with a noise mask, we succeeded in reducing this erroneous learning. Experiments verified the effectiveness of the proposed method in comparison with manual annotation, in terms of both the time needed to collect training data and the resulting detection accuracy of the vision system. The time required for data collection was reduced from 16.1 to 1.87 h. The accuracy of the vision system trained with the proposed method was 87.3%, which is higher than the accuracy of a vision system trained with the manual method.