Generative Adversarial Network based Cost-efficiency data Augmentation for AI Object Detection on De-palletizing Robots

摘要

The development of artificial intelligence (AI) has brought many changes to a variety of industrial fields. However, there are problems when applying AI due to the various environmental factors in industrial sites. In particular, the object detection model used for robot depalletizing often deteriorates over time due to the distribution of data changing every moment. While it is possible to maintain the performance of the model through additional data acquisition, continuous data acquisition is costly. Therefore, data augmentation techniques are used to reduce costs and maintain performance. However, data augmented by existing augmentation techniques do not significantly change in terms of the distribution of existing data, rendering it difficult to maintain the performance of the object detection model. In this paper, we develop a data augmentation pipeline based on generative adversarial networks, which is effective at maintaining performance and reducing the cost of object detection models. The proposed pipeline is composed of a data generator model and an object detection model. The generator model uses a small amount of training data to generate data with a new distribution, while the object detection model is trained with both training and generated data. The object detection model trained through the pipeline with 100 pieces of training data exhibited better performance on new data distribution by 9.9% AP compared to the model trained with 2000 pieces of training data. In addition, the results of the qualitative analysis confirmed that a representative error occurring in robot depalletizing could be improved by the proposed pipeline.