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Intersection over Union (IoU) is a key metric used to evaluate the accuracy of object detection models. It measures the overlap between the predicted bounding box and the ground truth bounding box. A higher IoU indicates better model performance in localizing objects.
Understanding IoU
IoU is calculated by dividing the area of overlap between the predicted and ground truth boxes by the area of their union. The formula is:
IoU = (Area of Overlap) / (Area of Union)
Values range from 0 to 1, where 0 means no overlap and 1 indicates perfect alignment.
Calculating IoU
To compute IoU, follow these steps:
- Determine the coordinates of the predicted and ground truth boxes.
- Calculate the area of each box.
- Find the coordinates of the intersection rectangle.
- Compute the area of the intersection.
- Calculate the area of the union by adding the areas of both boxes and subtracting the intersection area.
- Divide the intersection area by the union area to get the IoU score.
Using IoU for Model Evaluation
IoU is used to determine whether a detection is correct based on a threshold value, commonly set at 0.5. If the IoU exceeds this threshold, the detection is considered a true positive. Lower IoU thresholds may be used for more lenient evaluations, while higher thresholds demand more precise localization.
Metrics such as mean Average Precision (mAP) incorporate IoU thresholds to evaluate overall model performance across different object classes and detection confidence levels.