An Incremental Learning of YOLOv3 Without Catastrophic Forgetting for Smart City Applications
Qazi Mazhar ul Haq, Shanq-Jang Ruan, Muhamad Amirul Haq, Said Karam, Jeng Lun Shieh, Peter Chondro, De-Qin Gao
Abstract
Deep learning models have revealed outstanding performance on image classification and object detection tasks. However, there is a crucial drop in performance when they are subject to learn new data incrementally in the absence of previous training data. They suffer from catastrophic forgetting—abrupt drop in performance. This phenomenon affects the implementation of artificial intelligence in practical scenarios. To overcome catastrophic forgetting, the previous method has either saved previous data in memory or generated the previous data. However, these methods are computationally complex and infeasible for real-time applications. In this article, we proposed the YOLOv3 as an object detection framework for incremental learning. A knowledge distillation loss is introduced for the prediction of previously learned knowledge without utilizing previous training data. Consequently, these predictions are updated while learning the current model. Experimental results on the Pascal VOC2007 indicate that the proposed method significantly improved the mean average precision up to 74% for two classes in comparison to the state-of-the-art methods.