A Comparative Study of Nighttime Object Detection With Datasets From Australia and China

Intelligent traffic surveillance (ITS) in nighttime has increasingly become important in advanced-driving-assisted-systems (ADAS). ITS relies on efficient detection of objects and adaptability to changing environments in the nighttime. However, detecting the visual features of the objects in nighttime poses challenge due to insufficient illumination conditions. With the development of deep learning object detectors, higher accuracies have been achieved in object detection from nighttime images. In this paper, we employ convolutional neural network (CNN) object detectors such as Faster R-CNN (with various feature extractors), Feature Pyramid Network, and Single Shot Detector to analyse their performance in terms of speed and accuracy using our datasets for nighttime object detection. We carry out the image quality assessment of our datasets and perform an automatic parameter tuning for re-training the object detectors. Experimental results show that the Faster R-CNN with inception has higher detection performance compared with other CNN based detectors.