首页yolo和faster rcnn区别

yolo和faster rcnn区别

时间: 2023-04-23 18:02:46 浏览: 208

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.

YOLO (You Only Look Once) 和 Faster R-CNN 是两种目标检测算法。 YOLO 采用单次推理的方式，在单张图像上完成目标检测。其中，YOLO v3 是最新版本，具有更高的精度和更快的速度。 Faster R-CNN 则采用了两步推理的方式，首先通过区域提议网络 (Region Proposal Network, RPN) 提出目标候选区域，再通过后端网络 (Fast R-CNN) 进行目标检测。Faster R-CNN 具有较高的精度，但速度较慢。

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