目标检测之IoU(intersecton over union)标准

绿色代表ground truth box，红色代表predictive box.

ground truth box 需要手动标注，predictive box是你选用的模型计算出来的结果。

将两者放在一起用IOU来衡量我们的模型检测目标的有效性。

从下图你可以很直观的看到IoU的计算。

目标检测之IoU(intersecton over union)标准

从python编程的角度实现

def bb_intersection_over_union(boxA, boxB):

# determine the (x, y)-coordinates of the intersection rectangle

xA = max(boxA[0], boxB[0])

yA = max(boxA[1], boxB[1])

xB = min(boxA[2], boxB[2])

yB = min(boxA[3], boxB[3])

# compute the area of intersection rectangle

interArea = (xB - xA + 1) * (yB - yA + 1)

# compute the area of both the prediction and ground-truth rectangles

boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)

boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)

# compute the intersection over union by taking the intersection

# area and dividing it by the sum of prediction + ground-truth

# areas - the interesection area

iou = interArea / float(boxAArea + boxBArea - interArea)

# return the intersection over union value

return iou

目标检测之IoU(intersecton over union)标准

以上这正图片来自IJCV论文“weakly supervised localization and learning with generic knowledge”

图中黄色代表groundtruthbox，红色代表false positive 示例，绿色框代表 true positive，最右端的是未检测到的object，是FN的示例。

图中的Corloc=TP/(TP+FP)=2/3=66%.它与IOU存在的差别在于一个是正确的示例个数之比以及一个是面积之比。