Object Detection for Low Light Images
Dimitrios Mpouziotas, Eleftherios Mastrapas, Nikos Dimokas, Petros S. Karvelis, Euripidis Glavas · 2022
Object detection is a computer vision method for locating objects in images. Although, it has surpassed human performance and it has been considered practically solved, there are still considerable challenges, such as when photos are captured under suboptimal lighting conditions due to environmental and/or technical constraints. On the other hand, a variety of methods have been developed to enhance low light images, which can boost an object detector’s performance. In this work, we apply different image enhancement methods and study how they affect the efficacy of a well known detector (You Only Look Once, YOLO). A statistical analysis between YOLO’s performance for each enhancing algorithm, using a low light imaging dataset, is also presented, proving that for these kind of images, enhancement is a valuable step.