Adversarial Scene Reconstruction and Object Detection System for Assisting Autonomous Vehicle

TL;DR

This paper presents a deep learning system that reconstructs dark scenes to daylight and detects objects for autonomous vehicles, achieving high accuracy in scene reconstruction and understanding.

Contribution

It introduces a novel deep learning model that enhances scene reconstruction in dark conditions and improves object detection for autonomous driving.

Findings

87.3% accuracy in scene reconstruction

89.2% accuracy in scene understanding and detection

Effective in nighttime and dark visual environments

Abstract

In the current computer vision era classifying scenes through video surveillance systems is a crucial task. Artificial Intelligence (AI) Video Surveillance technologies have been advanced remarkably while artificial intelligence and deep learning ascended into the system. Adopting the superior compounds of deep learning visual classification methods achieved enormous accuracy in classifying visual scenes. However, the visual classifiers face difficulties examining the scenes in dark visible areas, especially during the nighttime. Also, the classifiers face difficulties in identifying the contexts of the scenes. This paper proposed a deep learning model that reconstructs dark visual scenes to clear scenes like daylight, and the method recognizes visual actions for the autonomous vehicle. The proposed model achieved 87.3 percent accuracy for scene reconstruction and 89.2 percent in scene understanding and detection tasks.