Real‐time object detection towards high power efficiency
Jincheng Yu1, Kaiyuan Guo1, Yiming Hu1, Xuefei Ning1, Jiantao Qiu1, Huizi Mao1, Song Yao2, Tianqi Tang1, Boxun Li1, Yu Wang1 and Huazhong Yang1
1Tsinghua University, Beijing, China
2DeePhi Technology, Beijing, China
ABSTRACT
In recent years, Convolutional Neural Network (CNN) has been widely applied in computer vision tasks and has achieved significant improvement in image object detection. The CNN methods consume more computation as well as storage, so GPU is introduced for real‐time object detection. However, due to the high power consumption of GPU, it is difficult to adopt GPU in mobile applications like automatic driving. The previous work proposes some optimizing techniques to lower the power consumption of object detection on mobile GPU or FPGA. In the first Low‐Power Image Recognition Challenge (LPIRC), our system achieved the best result with mAP/Energy on mobile GPU platforms. We further research the acceleration of detection algorithms and implement two more systems for real‐time detection on FPGA with higher energy efficiency. In this paper, we will introduce the object detection algorithms and summarize the optimizing techniques in three of our previous energy efficient detection systems on different hardware platforms for object detection.
