A Survey of Intelligent transportation systems based Modern Object Detectors Under Night-time Conditions

Object detection has progressed rapidly during the last few years and has become a highly significant area in computer vision. Amidst the rise of autonomous vehicles and smart traffic management systems, accurate vehicle detection under various lighting conditions has become paramount. This paper compares four state-of-the-art models Faster R-CNN, RetinaNet, YOLOv3 and YOLOv4 on how precise they detect vehicles under day and night-time scenarios. Experiments measure the 50% and 75% threshold average precision differences of multiple vehicle classes across two datasets. The results reveal a worse accuracy average of 15-20% and a maximum difference of 33% at night when compared to more illuminated day images.