Neighborhood Sampling Confidence Metric for Object Detection

Object detection using deep learning has recently gained significant attention due to its impressive results in a variety of applications, such as autonomous vehicles, surveillance, and image and video analysis. State-of-the-art models, such as YOLO, Faster-RCNN, and SSD, have achieved impressive performance on various benchmarks. However, it is crucial to ensure that the results produced by deep learning models are trustworthy, as they can have serious consequences, especially in an industrial context. In this paper, we introduce a novel confidence metric for object detection using neighborhood sampling. We evaluate our approach on MS-COCO and demonstrate that it significantly improves the trustworthiness of deep learning models for object detection. We also compare our approach against attribution-guided neighborhood sampling and show that such a heuristic does not yield better results.