首页faster-rcnn和yolov7谁检测精度高

faster-rcnn和yolov7谁检测精度高

时间: 2023-10-01 07:08:52 浏览: 115

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YOLOv7都是非常优秀的目标检测算法，它们的检测精度取决于具体的应用场景和数据集。一般来说，Faster R-CNN在小目标检测和密集目标检测方面表现更好，而YOLOv7在速度方面更具优势，适合于实时目标检测应用。因此，在选择算法时，需要根据具体的应用场景和需求来进行选择。

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