Multispectral Object Detection

Multispectral object detection aims to improve object identification accuracy and robustness by combining information from different parts of the electromagnetic spectrum, primarily visible (RGB) and thermal infrared (IR) imagery. Current research heavily utilizes deep learning architectures, particularly variations of the You Only Look Once (YOLO) family and transformer-based models, focusing on efficient feature fusion strategies and addressing challenges like modality misalignment and adverse weather conditions. This field is significant for its potential to enhance applications across diverse sectors, including autonomous driving, precision agriculture, and environmental monitoring, by providing more reliable and context-rich object detection capabilities regardless of lighting or weather.