Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems. (arXiv:2204.12402v1 [cs.CV])

Machine learning (ML)-enabled approaches are considered a substantial support
technique of detection and classification of obstacles of traffic participants
in self-driving vehicles. Major breakthroughs have been demonstrated the past
few years, even covering complete end-to-end data processing chain from sensory
inputs through perception and planning to vehicle control of acceleration,
breaking and steering. YOLO (you-only-look-once) is a state-of-the-art
perception neural network (NN) architecture providing object detection and
classification through bounding box estimations on camera images. As the NN is
trained …

arxiv cases cv edge impact ml systems understanding