Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Drivable region detection is challenging since various types of road, occlusion or poor illumination condition have to be considered in a outdoor environment, particularly at night. In the past decade, Many efforts have been made to solve these problems, hover, most of the already existing methods are designed for visible light cameras, which are inherently inefficient under low light conditions. ,  present a drivable region detection algorithm designed for thermal-infrared cameras in order to overcome the aforementioned problems. The novelty of the proposed method lies in the utilization of on-line road initialization with a highly scene-adaptive sampling mask. Furthermore, our prior road information extraction is tailored to enforce temporal consistency among a series of images. ,  also propose a large number of experiments in various scenarios (on-road, off-road and cluttered road). A total of about 6000 manually annotated images are made available in our bsite for the research community. Using this dataset,  compared our method against multiple state-of-the-art approaches including convolutional neural network (CNN) based methods to emphasize the robustness of our approach under challenging situations.
['Jae Shin Yoon', 'Kibaek Park', 'Soonmin Hwang', 'Namil Kim', 'Yukyung Choi', 'Francois Rameau', 'In So Kweon']
I am writing a research paper on a idea Thermal-infrared based drivable region detection
[{"from": "human", "value": "I am writing a research paper on a idea Thermal-infrared based drivable region detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Drivable region detection is challenging since various types of road, occlusion or poor illumination condition have to be considered in a outdoor environment, particularly at night. In the past decade, Many efforts have been made to solve these problems, hover, most of the already existing methods are designed for visible light cameras, which are inherently inefficient under low light conditions. ,  present a drivable region detection algorithm designed for thermal-infrared cameras in order to overcome the aforementioned problems. The novelty of the proposed method lies in the utilization of on-line road initialization with a highly scene-adaptive sampling mask. Furthermore, our prior road information extraction is tailored to enforce temporal consistency among a series of images. ,  also propose a large number of experiments in various scenarios (on-road, off-road and cluttered road). A total of about 6000 manually annotated images are made available in our bsite for the research community. Using this dataset,  compared our method against multiple state-of-the-art approaches including convolutional neural network (CNN) based methods to emphasize the robustness of our approach under challenging situations."}]