Dark Object Detection

Dark object detection research focuses on improving the accuracy and efficiency of object detection in low-light conditions, a challenging problem due to limited visual information and increased noise. Current approaches leverage deep learning architectures, including variations of convolutional neural networks (CNNs) and transformers, often incorporating techniques like Retinex theory and multi-scale feature enhancement to improve image quality and object representation before detection. These advancements are significant for applications such as autonomous driving and security systems, where reliable object detection is crucial even in challenging lighting conditions. Furthermore, a separate but related area investigates the detection of "dark patterns" – deceptive user interface designs in e-commerce – using natural language processing techniques like BERT to identify manipulative website elements.