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11 de abr. de 2023 · Artificial Intelligence (AI) in the automotive industry allows car manufacturers to produce intelligent and autonomous vehicles through the integration of AI-powered Advanced Driver Assistance Systems (ADAS) and/or Automated Driving Systems (ADS) such as the Traffic Sign Recognition (TSR) system. Existing TSR solutions focus on some categories of signs they recognise.
1 de ago. de 2023 · The rest of the paper is organized as follows: section 2 provides background knowledge about traffic signs recognition, section 3 describes the design process of the SNN–CNN hybrid network with RRAM-implemented weights, section 4 demonstrates the simulation results, section 5 concludes and prospects the presentation.
1 de oct. de 2022 · Traffic signs recognition results obtained by (a) the SignHRNet-lower and (b) the SignHRNet. Next, we removed the feature alignment module and its associated layers from the detection head, and directly used the outputs exported by the anchor regression terminal as the predicted bounding boxes.
Abstract: The automatic recognition of traffic signs is essential to autonomous driving, assisted driving, and driving safety. Currently, convolutional neural network (CNN) is the most popular deep learning algorithm in traffic sign recognition. However, the CNN cannot capture the poses, perspectives, and directions of the image, nor accurately recognize traffic signs from different perspectives.
Traffic-Sign-Recognition-using-CNN-and-Keras Using a 30,000-image dataset, we developed a CNN with Keras for traffic sign classification, and in 15 epochs, we achieved 98.3% accuracy. Tkinter GUI was implemented to allow for interactive picture classification, demonstrating proficiency with deep learning and model deployment.
In this article, we propose a trafic sign recognition (TSR) algorithm based on Faster R-CNN and YOLOv5. The road signs were detected from the driver’s point of view and the view was assisted by satellite images. First, we conduct image preprocessing by using guided image filtering for the input image to remove noises.
Built and trained a deep neural network to classify traffic signs, using PyTorch. The highlights of this solution would be data preprocessing, trained with heavily augmented data and using Spatial Transformer Network. - wolfapple/traffic-sign-recognition
