Incremental Object Detection Using Ensemble Modeling and Deep Transfer Learning

Abstract

Object detection is a subset of computer vision that can be accomplished using machine learning. The main process of object detection using machine learning model is model training with images containing objects of interest. However, the model training need a lot of training images. In addition, to improve the model ability to detect addition class of object, it need to be re-trained with both old and new image datasets. It is a time and computation consuming process. This paper proposes an incremental object detection model without re-training the old images. An ensemble model and transfer learning approach are used. The proposed model consist of three parts, two object detection sub-models and a decision model, which are a pre-trained model, a transferred-model and an ensemble model respectively. To illustrate the proposed model, the trained YOLO algorithm training with eighty object categories, 330,000 total images, from COCO image dataset is selected as the pre-trained model. It also be used as an initial model to train the transferred-model using transfer learning technique. Only new images are used for transferred-model training. The ensemble model with the bagging technique is used as a final classifier for choosing the best decision from both sub-models. Using our proposed model, the need of both the number of training dataset and the training time are reduced. Only several hours are needed for model training with three new object categories, 3,000 total images. The experimental results show that the proposed model achieve high performance on test image dataset with 93.33% accuracy.