This paper introduces C-DiffDet+, a novel object detection framework that enhances DiffusionDet by integrating global scene context with local proposal features. This approach significantly improves car damage detection, especially for challenging fine-grained damage like scratches and cracks, by leveraging context-aware fusion mechanisms.

This paper introduces Object-based Set Similarity (OSS), a novel metric designed to address computational costs and unreliable evaluations in active learning for real-world object detection. OSS quantifies active learning method effectiveness without requiring extensive detector training and improves evaluation robustness by identifying representative validation sets.

This paper introduces a deep learning solution for detecting small vehicle dents, which are often missed by traditional methods. It leverages the YOLOv8m object recognition framework and a custom-built dataset to achieve high accuracy and real-time performance for applications like automated insurance evaluations.

This study evaluates the performance of YOLOv7, an advanced object detection model, in identifying kitchen safety risks related to knife handling, specifically improper finger placement and blade contact with the hand. The findings highlight YOLOv7's strong potential for accurately detecting these hazards to improve kitchen safety.