Enhanced YOLO Algorithm for Robust Object Detection in Challenging Nighttime and Blurry, Low Vision

Abstract

In today's computer vision systems, the spread of object detection has been booming. Object detection in challenging conditions such as low-illumination or misty nights remains a difficult task, especially for one-stage detectors, which have limited improved solutions available. This approach improves upon existing one-stage models and excels in detecting objects in partially visible, and night environments. It segments objects using bounding boxes and tracks them in motion pictures. To detect an object in low-light environment we employ an RGB camera to generate a properly lighted image from an unilluminated image using dehazing and grayscale conversion methods. Secondly, low-illuminated images undergo dehazing and gray-scale conversion techniques to obtain a better-lighted image using the popular one-stage object detection algorithm YOLOv8. Video inputs are also taken for fast-moving vehicles; rates ranging from 5 frames per second to 160 frames per second could be efficiently predicted by YOLO-ODDT. All renowned object detectors are overshadowed in terms of speed and accuracy.