Empirical Upper Bound in Object Detection and More

TL;DR

This paper establishes the theoretical upper bounds of object detection accuracy, analyzes error sources, and examines model invariance properties to guide future improvements in detection models.

Contribution

It provides the first comprehensive upper bound estimates for object detection AP across datasets, characterizes dominant error types, and studies model invariance under various perturbations.

Findings

Upper bounds of AP are 91.6% on VOC, 78.2% on COCO, and 58.9% on OpenImages.

Classification errors dominate the error landscape in object detection.

Models rely heavily on context, especially for small object detection.

Abstract

Object detection remains as one of the most notorious open problems in computer vision. Despite large strides in accuracy in recent years, modern object detectors have started to saturate on popular benchmarks raising the question of how far we can reach with deep learning tools and tricks. Here, by employing 2 state-of-the-art object detection benchmarks, and analyzing more than 15 models over 4 large scale datasets, we I) carefully determine the upperbound in AP, which is 91.6% on VOC (test2007), 78.2% on COCO (val2017), and 58.9% on OpenImages V4 (validation), regardless of the IOU. These numbers are much better than the mAP of the best model1 (47.9% on VOC, and 46.9% on COCO; IOUs=.5:.95), II) characterize the sources of errors in object detectors, in a novel and intuitive way, and find that classification error (confusion with other classes and misses) explains the largest fraction of errors and weighs more than localization and duplicate errors, and III) analyze the invariance properties of models when surrounding context of an object is removed, when an object is placed in an incongruent background, and when images are blurred or flipped vertically. We find that models generate boxes on empty regions and that context is more important for detecting small objects than larger ones. Our work taps into the tight relationship between recognition and detection and offers insights for building better models.