Differences in Detection: Explainability Where it Matters

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

Differences in Detection (DnD) is an intuitive method to compare two object detection models directly, complementing existing metrics like mean Average Precision ($mAP$) and TIDE error analysis. DnD calculates the intersection of ground truth labels recognized by both models, along with corresponding difference sets and a complement set of missed labels. This provides a more direct, instance-level comparison than independent summary statistics, revealing individual and shared mistakes. The method integrates TIDE error types for detailed analysis within a confusion matrix. The authors demonstrate DnD's utility by comparing Mask R-CNN models with ConvNext-v2-B and ViTDet-B backbones on MS-COCO, and for robustness analysis of a Mask R-CNN with a FAN-S backbone on MS-COCO versus COCO-C with Gaussian Noise at severity level 3. DnD is particularly useful for guiding explainability methods like ODAM by providing structured subsets of relevant predictions. The code is publicly available.

Key takeaway

If you are evaluating and comparing object detection models for deployment or research, you should integrate Differences in Detection (DnD) into your analysis workflow. This method provides a direct, instance-level comparison of model errors and shared detections, offering deeper insights than $mAP$ or TIDE alone. Use DnD to identify specific failure modes, compare model robustness across datasets, and generate targeted examples for visual explainability tools like ODAM, optimizing your debugging and improvement efforts.

Key insights

DnD directly compares two object detection models by analyzing shared and differing ground truth detections, enhancing error analysis and explainability.

Principles

• Direct model comparison reveals specific error differences.
• Instance-level analysis complements summary metrics.
• Structured subsets improve explainability method targeting.

Method

Apply $mAP$ matching algorithm to two models' predictions. Calculate intersection ($B$), individual differences ($M1$, $M2$), and missed ($N$) ground truth sets. Integrate TIDE error types for detailed confusion matrix analysis.

In practice

• Compare Mask R-CNN backbones (ConvNext-v2-B vs. ViTDet-B).
• Analyze model robustness across datasets (MS-COCO vs. COCO-C).
• Guide ODAM explainability with metric-relevant examples.

Topics

• Object Detection
• Model Comparison
• Explainable AI
• TIDE Error Analysis
• MS-COCO Benchmark
• Robustness Analysis

Code references

• JohannesTheo/differences-in-detection
• MiXaiLL76/faster_coco_eval

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.