liu-runsen / yolov5-fastercnn
目标检测算法主要包括:两类two-stage和one-stage 一类是two-stage,two-stage检测算法将检测问题划分为两个阶段,首先产生候选区域(region proposals),然后对候选区域分类(一般还需要对位置精修),这一类的典型代表是R-CNN, Fast R-CNN, Faster R-CNN,Mask R-CNN家族。他们识别错误率低,漏识别率也较低,但速度较慢,不能满足实时检测场景。 另一类方式称为one-stage检测算法,其不需要region proposal阶段,直接产生物体的类别概率和位置坐标值,经过单次检测即可直接得到最终的检测结果,因此有着更快的检测速度,比较典型的算法如YOLOv5,SSD,RetinaNet等。 Faster…
☆9Updated 3 years ago
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