首页对比faster rcnn 与yolo

对比faster rcnn 与yolo

时间: 2023-10-01 21:06:13 浏览: 141

ssd yolo faster rcnn 对比

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.

Faster R-CNN和YOLO（You Only Look Once）都是目标检测领域的经典算法，但它们的设计和思路有所不同。 Faster R-CNN是一种two-stage的检测器，它首先使用一个Region Proposal Network（RPN）生成候选框，然后再对这些候选框进行分类和回归。与传统的R-CNN系列算法相比，Faster R-CNN使用RPN网络来代替了传统的Selective Search方法，使得整个检测流程更加高效和精确。 YOLO则是一种one-stage的检测器，它将整个检测流程作为一个单一的神经网络，直接将图片输入网络，同时输出所有检测目标的位置和类别。YOLO的优点是速度快，可以实现实时检测，但相对于Faster R-CNN，它的精度可能稍微低一些。总的来说，Faster R-CNN和YOLO都有各自的优点和缺点，具体使用哪种算法取决于具体的应用场景和需求。

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