Fix MeanAverageRecall: compute mAR@K using top-K detections per image (COCO-compliant)

[stop1one]

Description

This PR fixes the calculation of mAR@K in MeanAverageRecall to comply with the COCO evaluation protocol.
Previously, the implementation selected the top-K predictions globally across all images, rather than per image.
According to the COCO evaluation protocol, mAR@K should be calculated by considering the top-K highest-confidence detections for each image.

This issue is tracked in issue #1966

To resolve this, I modified the _compute and _compute_average_recall_for_classes function to first filter the statistics by confidence score before concatenating them and calculate the confusion matrix.

No new dependencies are required for this change.

Type of change

Please delete options that are not relevant.

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• This change requires a documentation update

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

I tested the change by running the metric on a dataset with varying numbers of predictions per image and verified that, for each image, only the top-K predictions (by confidence) were used in the mAR@K calculation.

Any specific deployment considerations

No special deployment considerations are required.

Docs

• Docs updated? What were the changes: N/A

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