Comments (2)
不好意思回复晚了。
理论上,CornerNet的检测中也是存在detection confidence和定位精度不匹配的问题。通过预测每个检测的定位精度,并与预测的detection confidence相乘得到final detection confidence可以在一定程度上缓解该问题。
但是CornerNet与RetinaNet和FCOS等直接预测检测框的检测模型不同。RetinaNet和FCOS直接在每个位置预测检测框,因此可直接添加IoU head为每个位置预测的检测框预测定位精度。但是CornerNet首先预测top-left corner和其对应的top-left corner embedding,bottom-right corner和其对应的bottom-right corner embedding,然后计算top 100 top-left corner和top 100 bottom-right corner之间的embedding的L1距离,距离小于0.5认为为候选的检测框,并通过soft-NMS去除重复检测框,即CornerNet在训练阶段未形成可直接使用的检测框,只有通过inference阶段的后处理才形成了有效的检测框。要实现IoU-aware CornerNet,问题在于:在inference阶段的后处理中才能形成有效的检测的情况下,如何预测CornerNet生成的检测框的定位精度?如果这个问题能够很好地解决,IoU-aware CornerNet应该会对模型有一定的提升
from iou-aware-single-stage-object-detector.
十分感谢您 如此细心的回复,拜读之后收益匪浅,我这就下去实验试试。
from iou-aware-single-stage-object-detector.
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from iou-aware-single-stage-object-detector.