IoU (Intersection over Union)

Intersection over Union (IoU) is a common evaluation metric used in the fields of computer vision and object detection to quantify the precision of an object detector's predictions. It measures the overlap between the predicted bounding box and the ground truth bounding box for a given object by calculating the ratio of their intersection area to their union area. IoU values range from 0 to 1, where 1 indicates perfect overlap and 0 indicates no overlap.

A higher IoU score signifies a higher accuracy of the object detection model. This metric is pivotal in comparing the performance of different models or algorithms on object detection and segmentation tasks, as it provides a clear and quantifiable measure of how closely the predicted bounding boxes match the actual locations and sizes of objects in the images.

In an autonomous driving system, IoU can be used to evaluate how accurately the system detects other vehicles on the road. The system's predictions, in the form of bounding boxes around detected vehicles, are compared to the ground truth bounding boxes manually annotated in the training dataset. An IoU score is calculated for each detection to assess the accuracy of the system. Similarly, in medical imaging, IoU can be used to evaluate the performance of models designed to identify and segment tumors in MRI scans.

The predicted segmentation of a tumor is compared with the ground truth segmentation provided by medical experts, and the IoU score indicates how well the model's predictions align with the expert annotations. High IoU scores in these examples would indicate that the models are performing well at accurately detecting and delineating objects of interest, which is crucial for the reliability and effectiveness of applications like autonomous driving and medical diagnosis.