Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. 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 200 traffic-sign categories represented in our novel dataset. The 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 a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.
PROJECT OUTPUT VIDEO:
System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 2 GB
Operating system : Windows 10.
Coding Language : Python
Domen Tabernik and Danijel Skoˇcaj, “Deep Learning for Large-Scale Traffic-Sign Detection and Recognition”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020.
Tag:best python projects, deep learning projects, deep learning projects for final year, ieee papers on python projects, ieee projects, ieee projects for cse, ieee projects for cse in python, machine learning projects, machine learning projects for final year, ml projects, python ai projects, python ieee projects, python ieee projects in machine learning, python machine learning projects