The Impact of Linear Motion Blur on the Object Recognition Efficiency of Deep Convolutional Neural Networks

摘要

Noise which can appear in images affects the classification performance of Convolutional Neural Networks (CNNs). In particular, the impact of linear motion blur, which is one of the possible noises, in the classification performance of CNNs is assessed in this work. A realistic vision sensor model has been proposed to produce a linear motion blur effect in input images. This methodology allows analyzing how the performance of several considered state of the art CNNs is affected. Experiments that have been carried out indicate that the accuracy is heavily degraded by a high length of the displacement, while the angle of displacement deteriorates the performance to a lesser extent.