Multispectral methods have gained considerable attention due to their promising performance across various fields. However, most existing methods cannot effectively utilize information from two modalities while optimizing time efficiency. These methods often prioritize accuracy or time efficiency, leaving room for improvement in their performance. To this end, we propose a new method bright channel prior attention for enhancing pedestrian detection in low-light conditions by integrating image enhancement and detection within a unified framework. The method uses the V-channel of the HSV image of the thermal image as an attention map to trigger the unsupervised auto-encoder for visible light images, which gradually emphasizes pedestrian features across layers. Moreover, we utilize unsupervised bright channel prior algorithms to address light compensation in low light images. The proposed method includes a self-attention enhancement module and a detection module, which work together to improve object detection. An initial illumination map is estimated using the BCP, guiding the learning of the self-attention map from the enhancement network to obtain more informative representation focused on pedestrians. The extensive experiments show effectiveness of the proposed method is demonstrated through.

The problem is to train a Deep Retinex-Net model on the dataset containing the pair of low and high light images and make the model be able to convert any given low light image into a high light image. The dataset named LOL (LOw Light paired) dataset used for the problem is taken from the above link. It contains 5000 low/normal light images pairs of different kinds such as household appliances, toys, books, garden, food items, playground, clubs, streets, etc. These raw images are resized to 128*128 and converted to Portable Network Graphics format. The below figure shows the subset of these images.

I expected to feel a little out of place at the swanky university event for bioethics. My wife, a professor with expertise in the field, had invited me. But when I introduced myself as a software engineer to the attendees, many wanted to talk about one thing -- artificial intelligence (AI). How would it affect society? What are the goods and the bads?

I expected to feel a little out of place at the swanky university event for bioethics. My wife, a professor with expertise in the field, had invited me. But when I introduced myself as a software engineer to the attendees, many wanted to talk about one thing -- artificial intelligence (AI). How would it affect society? What are the goods and the bads?