目标检测算法的介绍
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.
Fast R-CNN相比于YOLO的优点是,其定位准确度更高,同时能够生成更好的真实边界框(RoI);但其缺点是速度较慢,需要进行两个模型之间的交互,耗费时间和计算资源。而YOLO则是一种单级的目标检测框架,速度较快;但其缺点是准确度较低,特别是对于小目标的检测能力较弱。