Comparing machine learning and neural network-based approaches for sign detection and classification in autonomous vehicles
Department of Electrical and Computer Engineering
We compare two algorithms, Histogram of Oriented Gradient (HOG) with linear Support Vector Machine (SVM) and You Look Only Once (YOLO), to the task of sign detection and classification from imagery from the LISA dataset. Comparisons are made in terms of execution time, accuracy, and readiness for use on GPU or FPGA hardware for acceleration. We find the neural network-based approaches like YOLO have superior accuracy but run slower on general purpose CPUs without acceleration. On the other hand, while less accurate the SVM-based are faster without acceleration.
Proceedings of SPIE - The International Society for Optical Engineering
Comparing machine learning and neural network-based approaches for sign detection and classification in autonomous vehicles.
Proceedings of SPIE - The International Society for Optical Engineering,
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/2734
© 2020 SPIE. Publisher’s version of record: https://doi.org/10.1117/12.2558966