MRNet: A Competition model for MMSP on Embedded Deep Learning Object Detection

Object detection in computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in deep learning based algorithms, it is hard to run these models on embedded systems, which have limited computing capabilities. In response to this situation, we combine the idea of MobileNet to improve RetinaNet, design a lightweight deep learning model MRNet which not only suitable for embedded systems but also achieve high accuracy. The model was demonstrated in the MMSP 2019 Embedded Deep Learning Object Detection Model Competition. In the comprehensive evaluation of the model size, computational complexity and running speed on TX2. our proposed method won the third place in the competition.