首页faster rcnn 和yolo 区别

faster rcnn 和yolo 区别

时间: 2023-10-02 20:05:09 浏览: 114

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 都是目标检测算法，但它们的设计和实现方式有所不同。 Faster R-CNN 采用两个阶段的检测方法，即先通过一个特征提取网络（如 VGG、ResNet 等）产生特征图，再通过 Region Proposal Network（RPN）生成一系列候选框，最后对这些候选框进行分类和回归。相比于传统的两阶段目标检测算法，Faster R-CNN 的速度更快，但在小目标检测方面表现不如 YOLO。 YOLO（You Only Look Once）采用单阶段的检测方法，即将特征图分成网格，每个网格预测出多个候选框，然后对这些候选框进行分类和回归。相比于 Faster R-CNN，YOLO 的速度更快，但在大目标检测和目标定位方面表现不如 Faster R-CNN。总体而言，Faster R-CNN 和 YOLO 在不同的场景下具有各自的优势，选择哪种算法取决于具体应用的需求。

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