Ground Pedestrian and Vehicle Detections Using Imaging Environment Perception Mechanisms and Deep Learning Networks

Abstract

In order to build a robust network for the unmanned aerial vehicle (UAV)-based ground pedestrian and vehicle detection with a small number of training datasets but strong luminance environment adaptability, a system that considers both environment perception computation and a lightweight deep learning network is proposed. Because the visible light camera is sensitive to complex environmental lights, the following computational steps are designed: First, entropy-based imaging luminance descriptors are calculated; after image data are transformed from RGB to Lab color space, the mean-subtracted and contrast-normalized (MSCN) values are computed for each component in Lab color space, and then information entropies were estimated using MSCN values. Second, environment perception was performed. A support vector machine (SVM) was trained to classify the imaging luminance into excellent, ordinary, and severe luminance degrees. The inputs of SVM are information entropies; the output is the imaging luminance degree. Finally, six improved Yolov3-tiny networks were designed for robust ground pedestrian and vehicle detections. Extensive experiment results indicate that our mean average precisions (MAPs) of pedestrian and vehicle detections can be better than ~80% and ~94%, respectively, which overmatch the corresponding results of ordinary Yolov3-tiny and some other deep learning networks.