Metamorphic Object Insertion for Testing Object Detection Systems

Abstract

Recent advances in deep neural networks (DNNs) have led to object detectors (ODs) that can rapidly process pictures or videos, and recognize the objects that they contain. Despite the promising progress by industrial manufacturers such as Amazon and Google in commercializing deep learning-based ODs as a standard computer vision service, ODs --- similar to traditional software --- may still produce incorrect results. These errors, in turn, can lead to severe negative outcomes for the users. For instance, an autonomous driving system that fails to detect pedestrians can cause accidents or even fatalities. However, despite their importance, principled, systematic methods for testing ODs do not yet exist.