ResCoNN: ResourceEfficient FPGAAccelerated CNN for Traffic Sign Classification Precise classification and detection of (distorted and normal) traffic signs in realtime is one of the nontrivial requirements for safe autonomous driving. The stateoftheart convolutional neural networks (CNNs) for traffic sign detection though accurate are resourcehungry due to their inherent structure with massive networks with millions of fullprecision parameters making them infeasible for lowend FPGA platforms leading to higher implementation costs. The existing works employing lowprecision bits and similar techniques though hardware friendly leads to degradation in accuracy. In this work, we propose a resourceefficient CNN (ResCoNN) architecture with a small number of weights (only 60,000 compared to a few million in stateoftheart CNNs) and employ for traffic sign detection and classification. For increased efficiency, the network takes advantage of binary weights and integer activations, rather than employing complex computations like Batch Normalization and Exponential Linear Units. ResCoNN achieves a classification accuracy of u003e 96% on realworld images at a framerate of 36fps on a Zynq SoC (xc7z020clg4841) with 90% reduced weights compared to stateoftheart CNNs.

Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition Onboard vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and realworld samples from known classes to perform synthetictoreal domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIATS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio     xe2x88xbc 1  4     for newknown classes; even for more challenging ratios such as     xe2x88xbc 4  1    , the results are also very positive.