Identification of Fine-grained Systematic Errors via Controlled Scene Generation

arXiv:2404.07045v1 Announce Type: new
Abstract: Many safety-critical applications, especially in autonomous driving, require reliable object detectors. They can be very effectively assisted by a method to search for and identify potential failures and systematic errors before these detectors are deployed. Systematic errors are characterized by combinations of attributes such as object location, scale, orientation, and color, as well as the composition of their respective backgrounds. To identify them, one must rely on something other than real images from a test …

abstract applications arxiv autonomous autonomous driving cs.cv detectors driving errors fine-grained identification identify object safety safety-critical search type via