A Thorough Evaluation of Kernel Order in CNN Based Traffic Signs Recognition - de Oliveira, Lucas; Mota, Guilherme; Vidal, Vitor (Anais do Workshop de Visão Computacional (WVC), 2020)
Convolutional Neural Network is an important deep learning architecture for computer vision. Alongside with its variations, it brought image analysis applications to a new performance level. However, despite its undoubted quality, the evaluation of the performance presented in the literature is mostly restricted to accuracy measurements. So, considering the stochastic characteristics of neural networks training and the impact of the architectures configuration, research is still needed to affirm if such architectures reached the optimal configuration for their focused problems. Statistical significance is a powerful tool for a more accurate experimental evaluation of stochastic processes. This paper is dedicated to perform a thorough evaluation of kernel order influence over convolutional neural networks in the context of traffic signs recognition. Experiments for distinct kernels sizes were performed using the most well accepted database, the socalled German Traffic Sign Recognition Benchmark.