Thermal-Based Vehicle Detection System using Deep Transfer Learning under Extreme Weather Conditions
Shaima Alhammadi, Sabeha A. Alhameli, Fatima A. Almaazmi, Bashayer H. Almazrouei, Hyam A. Almessabi, Yasmeen Abu-Kheil · 2022
The future of autonomous driving is fast approaching. As driving assistance and safety features increase, basic driving tasks will be automated, eliminating the requirement for human judgement and interaction. In today's world, features such as adaptive cruise control and lane departure warnings have become common. Using predictive ride technology, the car senses weather conditions and adjusts its ride settings to keep passengers comfortable on bumpy or uneven roads and to reduce travel sickness. This paper presents a thermal based vehicle detection system for autonomous car testing under extreme weather conditions. We utilize transfer learning by using three pre-trained convolutional neural networks and trained them on our data. The three networks were (GoogleNet, ResNet-50, SqueezeNet). The pre-trained networks showed different performance in terms of validation accuracy and frequency of alteration. The ResNet-50 network has the best performance, which shows a validation accuracy of 100%.