ClipGrader leverages vision-language models (CLIP) to automatically assess the quality of bounding box annotations in object detection systems, reducing the need for costly manual verification.

• Evaluates both class label correctness and spatial precision of bounding boxes
• Provides a cost-effective alternative to manual annotation verification
• Creates more reliable training datasets for object detection models
• Particularly valuable for security applications where detection accuracy is critical

This research significantly improves security systems by ensuring higher quality annotations for surveillance, monitoring, and threat detection models - critical in scenarios where false negatives or positives could have serious consequences.

ClipGrader: Leveraging Vision-Language Models for Robust Label Quality Assessment in Object Detection