Embedded YOLO: Faster and lighter object detection

Abstract

Object detection is a fundamental but very important task in computer vision. Most current algorithms require high computing resources, which hinders their deployment on embedded system. In this research, we propose a neural network model named Embedded YOLO to solve this problem. We propose the DSC_CSP module to replace the middle layers of YOLOv5s to reduce the number of model parameters. On the other hand, in order to avoid the decrease of performance due to the reduction of parameters, we utilize knowledge distillation to maintain performance. To make good use of the information provided by data augmentation, we propose a new method called Dynamic Interpolation Mosaic to improve the original Mosaic. Due to serious imbalance in the number of samples of different data types, we employ a two-stage training scheme to overcome the data imbalance problem. The proposed model achieved the best results in the ICMR2021 Grand Challenge PAIR Competition with 0.59 mAP and model size of 12MB and 41 FPS on the MediaTek's Dimensity 1000 platform. These results confirm that the proposed model is suitable for deployment in embedded systems for object detection task.