We summarize our contributions as follows:
– We introduce SSD, a single-shot detector for multipl...
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.