2020/10/30 by Marcel Sheeny, Sheeny, Marcel
Computer Science · Engineering · Physics and Astronomy · #Advanced Optical Sensing Technologies #Adverse weather #Artificial intelligence #Computer science #Computer vision #Deep learning #Environmental science #Geography #Geology #Infrared Target Detection Methodologies #Lidar #Meteorology #Non-Invasive Vital Sign Monitoring #Radar #Radar imaging #Remote sensing #Robustness (evolution) #Severe weather #Snow #Speckle noise #Speckle pattern #Storm #Telecommunications #Weather radar #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2010.16285
published in arXiv (Cornell University) (Cornell University) · 201 pages, PhD Thesis, Heriot-Watt University, October 2020. Includes results from arXiv:1804.02576, arXiv:1912.03157, https://www.mdpi.com/2076-3417/10/11/3861 and arXiv:2010.09076
arxiv created 2020/10/30 · openalex publication_date 2020/10/30 · arxiv updated 2020/11/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Autonomous cars are an emergent technology which has the capacity to change human lives. The current sensor systems which are most capable of perception are based on optical sensors. For example, deep neural networks show outstanding results in recognising objects when used to process data from cameras and Light Detection And Ranging (LiDAR) sensors. However these sensors perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This thesis explores new sensing developments based on long wave polarised infrared (IR) imagery and imaging radar to recognise objects. First, we developed a methodology based on Stokes parameters using polarised infrared data to recognise vehicles using deep neural networks. Second, we explored the potential of using only the power spectrum captured by low-THz radar sensors to perform object recognition in a controlled scenario. This latter work is based on a data-driven approach together with the development of a data augmentation method based on attenuation, range and speckle noise. Last, we created a new large-scale dataset in the "wild" with many different weather scenarios (sunny, overcast, night, fog, rain and snow) showing radar robustness to detect vehicles in adverse weather. High resolution radar and polarised IR imagery, combined with a deep learning approach, are shown as a potential alternative to current automotive sensing systems based on visible spectrum optical technology as they are more robust in severe weather and adverse light conditions.