19,314 research outputs found
Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods
This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad TIN2013-46801-C4-1-
Over speed detection using Artificial Intelligence
Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results
Convolutional Color Constancy
Color constancy is the problem of inferring the color of the light that
illuminated a scene, usually so that the illumination color can be removed.
Because this problem is underconstrained, it is often solved by modeling the
statistical regularities of the colors of natural objects and illumination. In
contrast, in this paper we reformulate the problem of color constancy as a 2D
spatial localization task in a log-chrominance space, thereby allowing us to
apply techniques from object detection and structured prediction to the color
constancy problem. By directly learning how to discriminate between correctly
white-balanced images and poorly white-balanced images, our model is able to
improve performance on standard benchmarks by nearly 40%
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning
Understanding the global optimality in deep learning (DL) has been attracting
more and more attention recently. Conventional DL solvers, however, have not
been developed intentionally to seek for such global optimality. In this paper
we propose a novel approximation algorithm, BPGrad, towards optimizing deep
models globally via branch and pruning. Our BPGrad algorithm is based on the
assumption of Lipschitz continuity in DL, and as a result it can adaptively
determine the step size for current gradient given the history of previous
updates, wherein theoretically no smaller steps can achieve the global
optimality. We prove that, by repeating such branch-and-pruning procedure, we
can locate the global optimality within finite iterations. Empirically an
efficient solver based on BPGrad for DL is proposed as well, and it outperforms
conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the
tasks of object recognition, detection, and segmentation
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