TY - JOUR
T1 - Low-illumination image enhancement for night-time UAV pedestrian detection
AU - Wang, Weijiang
AU - Peng, Yeping
AU - Cao, Guangzhong
AU - Guo, Xiaoqin
AU - Kwok, Ngaiming
PY - 2021
Y1 - 2021
N2 - To accomplish reliable pedestrian detection using unmanned aerial vehicles (UAVs) under night-time conditions, an image enhancement method is developed in this article to improve the low-illumination image quality. First, the image brightness is mapped to a desirable level by a hyperbolic tangent curve. Second, the block-matching and 3-D filtering methods are developed for an unsharp filter in YCbCr color space for image denoising and sharpening. Finally, pedestrian detection is performed using a convolutional neural network model to complete the surveillance task. Experimental results show that the Minkowski distance measurement index of enhanced images is increased to 0.975, and the detection accuracies, in F-measure and confidence coefficient, reach 0.907 and 0.840, respectively, which are the highest as compared with other image enhancement methods. This developed method has potential values for night-time UAV visual monitoring in smart city applications.
AB - To accomplish reliable pedestrian detection using unmanned aerial vehicles (UAVs) under night-time conditions, an image enhancement method is developed in this article to improve the low-illumination image quality. First, the image brightness is mapped to a desirable level by a hyperbolic tangent curve. Second, the block-matching and 3-D filtering methods are developed for an unsharp filter in YCbCr color space for image denoising and sharpening. Finally, pedestrian detection is performed using a convolutional neural network model to complete the surveillance task. Experimental results show that the Minkowski distance measurement index of enhanced images is increased to 0.975, and the detection accuracies, in F-measure and confidence coefficient, reach 0.907 and 0.840, respectively, which are the highest as compared with other image enhancement methods. This developed method has potential values for night-time UAV visual monitoring in smart city applications.
UR - https://hdl.handle.net/1959.7/uws:65001
U2 - 10.1109/TII.2020.3026036
DO - 10.1109/TII.2020.3026036
M3 - Article
SN - 1551-3203
VL - 17
SP - 5208
EP - 5217
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 8
ER -