Vehicle Detection in Thermal Images Using Deep Neural Network 研究生: 張晉瑋 學號: M10515018 指導

碩士 === 國立臺灣科技大學 === 資訊工程系 === 106 === In today’s world, it becomes critical for a self-driving car to detect the vehicles irrespective of it being a day or night. During the night time, the RGB images captured by the cameras in the self-driving cars are not clear. Further, to overcome this issue, we...

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Bibliographic Details
Main Authors: Chin-Wei Chang, 張晉瑋
Other Authors: Kai-Lung Hua
Format: Others
Language:en_US
Published: 2018
Online Access:http://ndltd.ncl.edu.tw/handle/n2uudh
Description
Summary:碩士 === 國立臺灣科技大學 === 資訊工程系 === 106 === In today’s world, it becomes critical for a self-driving car to detect the vehicles irrespective of it being a day or night. During the night time, the RGB images captured by the cameras in the self-driving cars are not clear. Further, to overcome this issue, we propose a real-time vehicle detection using a sequence of night-time thermal images. Moreover, the thermal images have the capability of retaining even the minuscule vehicle details in a dim environment. For an efficient vehicle detection, the thermal image dataset collected during the dusk and night is used for training purposes. Subsequently, the contrast enhancement and sharpening of these images are performed using the Thermal Feature Enhancement (TFE). Then the concatenated images are supplied as the input to allow the model to learn more effectively. Besides, we also propose an improved convolution network model entitled as the Thermal Image Only Looked Once (TOLO) model for vehicle detection. Additionally, juddering of the moving vehicle results in blurred images that are referred to as low probability candidates. Also, we propose a method called as Low Probability Candidate Filter (LPCF) to overcome this problem. Our proposed method produces better results for the F1-measure in comparison with existing methods.