The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
This provides a new dataset for researchers studying object localization and behavior understanding in thermal drone imagery, but it is incremental as it builds on existing thermal drone datasets by adding multimodal features.
The authors introduced MONET, a multimodal thermal drone dataset capturing human and vehicle activities in rural areas with approximately 53K images and 162K annotated bounding boxes, and evaluated nine object detection algorithms to identify challenges in transfer learning between sites.
We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MONET to study the problem of object localisation and behaviour understanding of targets undergoing large-scale variations and being recorded from different and moving viewpoints. Target activities occur in two different land sites, each with unique scene structures and cluttered backgrounds. MONET consists of approximately 53K images featuring 162K manually annotated bounding boxes. Each image is timestamp-aligned with drone metadata that includes information about attitudes, speed, altitude, and GPS coordinates. MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata. We assessed the difficulty of the dataset in terms of transfer learning between the two sites and evaluated nine object detection algorithms to identify the open challenges associated with this type of data. Project page: https://github.com/fabiopoiesi/monet_dataset.