Abstract
Object detection is an essential function for mobile robots, allowing them to carry out missions efficiently. In recent years, various deep learning models based on convolutional neural networks have achieved good performance in object detection. However, in cases in which robots have to carry out missions in a particular environment, utilizing a model that has been trained without considering the environment in which robots must conduct their tasks degrades their object detection performance, leading to failed missions. This poor model accuracy occurs because of the class imbalance problem, in which the occurrence frequencies of the object classes in the training dataset are significantly different. In this study, we propose a systematic solution that can solve the class imbalance problem by training multiple object detection models and using these models effectively for robots that move through various environments to carry out missions. Moreover, we show through experiments that the proposed multi-model-based object detection framework with environment-context awareness can effectively overcome the class imbalance problem. As a result of the experiment, CPU usage decreased by 45.49% and latency decreased by more than 60%, while object detection accuracy increased by 6.6% on average.