A Method to Automatic Create Dataset for Training Object Detection Neural Networks
Shi Sheng Zhou, Zijun Yang, Miaomiao Zhu, He L Li, Seiichi Serikawa, Mitsunori Mizumachi, Lifeng Zhang · IEEE Access · 2022
Numerous high-accuracy neural networks for object detection have been proposed in recent years. Creating a specific training dataset seems to be the main obstacle in putting them into practice. This paper aims to overcome this obstacle and proposes an automatic dataset creation method. This method first extracts objects from the source images and then combines them as synthetic images. These synthetic images are automatic annotated using the data flow in the process and can be used directly for training. To the best of our knowledge, this is the first automatic dataset creation method for object detection tasks. In addition, the adaptive object extraction method and created natural synthetic images make the proposed method maintain strong adaptation and generalization ability. To validate the feasibility, a dataset that includes 44 categories of objects is created for the object detection task in a vending supermarket. Under the strictest metricAP75, the trained EfficientDet achieves 98.7% accuracy on the common difficulty testing set and 93.1% accuracy on the high difficulty testing set.