Performance of Different Optimizers for Traffic Sign Classification
Ishita Joshi, Milauni Desai, Sannidhi Bookseller, Rucha Mohod, Chirag N. Paunwala, Bhaumik Vaidya · 2019
The use of traffic signs is vital especially when travelling in the highways or the hills in adverse environmental conditions. The pace of advancements in the field of Machine learning has opened doors for scopes of improvement in the performance of Convolutional Neural Network Architectures dedicated to classification of traffic signs. Speed is as important as accuracy for such problems in the field of Advance Driver Assistance Systems and the use of GPU instead of CPU gives the benefit of parallel processing. Gradient Descent helps navigating towards the minima of the loss function. Purpose of various gradient descent optimizing algorithms is to help in quicker convergence. This proposed algorithm comprises of a compact Convolutional Neural Network architecture that was trained on GPU using RMSProp, Adam and Nadam optimizers on the BelgiumTS dataset. RMSProp and Adam caused either underfitting or over-fitting that was resolved by Nadam used with an appropriate dropout with 97.51 training accuracy and 96.78 testing accuracy. The predictions on test images convey that the architecture trained using Nadam works perfectly for blurry images, positionally challenging images and images with uneven illumination.