Correct confusion matrix calculation-function evaluate_detection_batch

[panagiotamoraiti]

Description

This fixes the issue where predicted bounding boxes were matched to ground truth boxes solely based on IoU, without considering class agreement during the matching process. Currently, if a predicted box has a higher IoU but the wrong class, it gets matched first, and the correct prediction with the right class but lower IoU is discarded. This leads to miscounting true positives and false positives, resulting in inaccurate confusion matrix.

The change modifies the matching logic (method evaluate_detection_batch) to incorporate both IoU and class agreement simultaneously, ensuring only predictions that match both IoU threshold and class are matched to ground truths. This results in a correct confusion matrix.

Type of change

• Bug fix (non-breaking change which fixes an issue)

How has this change been tested, please provide a testcase or example of how you tested the change?

I had an image with 2 TP and 1 FP detections, but the confusion matrix predicted 1 TP, 2 FP and 1FN. The FP bbox with the wrong class had higher overlap so the TP was discarded. At the end also this bbox was discareded due to the wrong class id. Now my confusion matrix predicts correctly 2 TP and 1 FP detections.

I run this in a big dataset, another script i have developed and used extensively in previous project gives the following results that now match with the confusion matrix, before i corrected them they didn't match.

Test Set:
Ground Truth Objects: 481
True Positives: 469
False Positives: 11
False Negatives: 12

Validation Set:
Ground Truth Objects: 1073
True Positives: 1037
False Positives: 23
False Negatives: 36

Train Set:
Ground Truth Objects: 3716
True Positives: 3674
False Positives: 52
False Negatives: 42

[CLAassistant]

All committers have signed the CLA.