embelishTo fully evaluate the segmentation performance of REA-Net for rail surface defects four common metrics in-clude Precision P Recall R F1 score and Intersection over Union IoU are selected as th

These metrics are commonly used in evaluating the performance of segmentation algorithms. Precision measures the proportion of correctly classified positive samples out of all samples predicted as positive. Recall measures the proportion of correctly classified positive samples out of all actual positive samples. F1 score is the harmonic mean of precision and recall, providing a balanced evaluation of the algorithm's performance. Intersection over Union measures the overlap between the predicted and ground truth regions, providing an indication of how well the algorithm captures the true extent of the defects. By considering these metrics, the segmentation performance of REA-Net can be thoroughly assessed and compared with other algorithms or techniques