Jaimin Nitesh Shah
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In recent years, research into autonomous vehicles has received substantial attention which has accelerated the development and adaptation of these vehicles in our day to day world. However, there are still major pitfalls that need to be solved before these vehicles are to become fully driver-less on public streets. These self-driving vehicles use image recognition algorithms that have been trained to detect objects such as streets, stop signs, and people. However, in bad weather conditions, these objects become increasingly difficult to detect. As such, all things inclement weather related with object detection and image segmentation are a major focus in research. Imagine if you are driving in an adverse weather with a lot of snow to lessen your visibility of the surroundings, how do you drive without knowing what’s ahead? Therefore, this proposed work aims to solve these problems to help drive in adverse weather conditions. Image dehazing also plays an important role in climatology, environmental perception wildlife monitoring and conservation, surveillance systems, object detection and recognition. The proposed work will provide more opportunities to explore different models in the field of image denoising.
Van Tam Nguyen
Primary Advisor's Department
Stander Symposium Posters, College of Arts and Sciences
United Nations Sustainable Development Goals
Sustainable Cities and Communities; Industry, Innovation, and Infrastructure
"Image Dehazing for Autonomous Driving in Inclement Weather Conditions" (2020). Stander Symposium Projects. 2007.