An Analysis of Cloud-Based Machine Learning Models for Traffic-Sign Classification
Abstract: The machine learning method deep neural networks are commonly used for artificial intelligence applications such as speech recognition, robotics, and computer vision. Deep neural networks often have very good accuracy, the downside is the complexity of the computations. To be able to use deep neural network models on devices with less computing power, such as smart-phones e.g., can the model run on the cloud and send the results to the device. This thesis will evaluate the possibility to use a smart-phone as a camera unit with Googles open source neural network called Inception, to identify traffic signs. The thesis analyzes the possibility to move the computation to the cloud and still use the system for real-time applications, and compare it to running the image model on the edge (the device itself). The accuracy of the model, as well as how estimations of future 5G mobile networks will affect the quality of service for the system is also analyzed. The result shows that the model achieved an accuracy of 88.0 % on the "German traffic sign benchmark" data set and 97.6 % on a newly created data set (data sets of images to test the neural network model on). The total time when using this system, from sending the image to receiving the result, is > 2 s. Because of this can it not be used for any application affecting traffic safety. Estimated improvements from future 5G mobile networks could include reduced communication delay, ultra-reliable communication, and with the higher bandwidth available could the system achieve a higher capacity if that would be required e.g. sending higher quality images.
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