Adversarial Scene Reconstruction and Object Detection System for Assisting Autonomous Vehicle
This addresses scene classification challenges in autonomous vehicles, particularly in low-light conditions, but appears incremental as it builds on existing deep learning methods for visual tasks.
The paper tackled the problem of visual classifiers struggling with dark scenes and scene context identification, especially at night, by proposing a deep learning model that reconstructs dark scenes to daylight-like clarity and recognizes visual actions for autonomous vehicles, achieving 87.3% accuracy in scene reconstruction and 89.2% in scene understanding and detection.
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.