TY - GEN
T1 - Vehicle Detection in Thermal Images Using Deep Neural Network
AU - Chang, Chin Wei
AU - Srinivasan, Kathiravan
AU - Chen, Yung Yao
AU - Cheng, Wen-Huang
AU - Hua, Kai Lung
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In today's world, it becomes critical for a self-driving car to detect the vehicles irrespective of it being a day or night. 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 efcient 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 efectively. Besides, we also propose an improved convolution network model entitled as the Thermal Image Only Looked Once (TOLO) model for vehicle detection. Additionally, we propose a method called as Low Probability Candidate Filter (LPCF) to compensate the probability of not-easy-to-detect vehicles. Our proposed method produces better results for the F1-measure in comparison with existing methods.
AB - In today's world, it becomes critical for a self-driving car to detect the vehicles irrespective of it being a day or night. 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 efcient 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 efectively. Besides, we also propose an improved convolution network model entitled as the Thermal Image Only Looked Once (TOLO) model for vehicle detection. Additionally, we propose a method called as Low Probability Candidate Filter (LPCF) to compensate the probability of not-easy-to-detect vehicles. Our proposed method produces better results for the F1-measure in comparison with existing methods.
UR - http://www.scopus.com/inward/record.url?scp=85065419315&partnerID=8YFLogxK
U2 - 10.1109/VCIP.2018.8698741
DO - 10.1109/VCIP.2018.8698741
M3 - Conference contribution
AN - SCOPUS:85065419315
T3 - VCIP 2018 - IEEE International Conference on Visual Communications and Image Processing
BT - VCIP 2018 - IEEE International Conference on Visual Communications and Image Processing
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2018 through 12 December 2018
ER -