Low-Illumination Image Enhancement for Night-Time UAV Pedestrian Detection

作者：Weijiang Wang, Yeping Peng, Guang‐Zhong Cao, Xiaoqin Guo, Ngaiming Kwok · 发表于：IEEE Transactions on Industrial Informatics · 年份：2020 · DOI：10.1109/tii.2020.3026036 · 被引用次数：62 · 研究领域：Image Enhancement Techniques、Video Surveillance and Tracking Methods、Advanced Image Fusion Techniques

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.