Pinned Repositories
InstaVerse
KeyMIDI
A python Script which allows to use Numpad as MIDI Pad
SimpleBible
Android App currently in development. alpha version is released
TeluguKeyboard
A Simple program which is used to type in Telugu (Unicode). Only works on Windows
Traffic-Sign-Detection
In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the Mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 4 traffic-sign categories represented in our novel dataset. Results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of traffic-sign inventory management.
goldnjohn's Repositories
goldnjohn/SimpleBible
Android App currently in development. alpha version is released
goldnjohn/InstaVerse
goldnjohn/KeyMIDI
A python Script which allows to use Numpad as MIDI Pad
goldnjohn/TeluguKeyboard
A Simple program which is used to type in Telugu (Unicode). Only works on Windows
goldnjohn/Traffic-Sign-Detection
In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the Mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 4 traffic-sign categories represented in our novel dataset. Results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of traffic-sign inventory management.