When Deep Learning Meets Geometry for Air-to-Ground Perception on Drones: 2nd Edition

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have witnessed a remarkable progress in both military and civilian applications. The evolution of computer vision, especially the advancement of deep learning, has revolutionized UAV capabilities, enabling them to execute complex visual tasks such as object detection, tracking, segmentation, and location. The multi - sensor cameras mounted on UAVs provide a rich source of multi-modal data, enhancing the diversity and depth of information available for analysis. Moreover, the recent emergence of large vision models has further propelled innovation in UAV-related visual tasks, offering more powerful tools for processing and interpreting visual data. Drawing inspiration from these advancements, our special issue focuses on visual tasks from a UAV perspective, encompassing detection, tracking, segmentation, and location. It emphasizes the extensive application of multi-modal information fusion and we strongly encourage the application of visual based large models to push the frontiers of what UAVs can achieve in visual tasks. This special issue welcomes cutting-edge research in relevant fields, aiming to foster further development and innovation.

Recently, drones are drawing increasing attention as data acquisition or aerial perception platforms for many civilian or military applications. Owing to the success of deep learning in computer vision, drone images are processed in an end-to-end manner to achieve air-to-ground perception (e.g., detection, tracking, recognition). The multi - sensor cameras mounted on droness provide a rich source of multi-modal data, enhancing the diversity and depth of information available for analysis. This Special Issue aims at boosting deep learning based air-to-ground perception performance with multi-modal information fusion of both visual images and geometric metadata from drones. We welcome submissions which provide the community with the most recent advancements regarding this Special Issue.

Topics of interest include, but are not limited to, the following:

• Air-to-ground object detection for drones
• Air-to-ground single/multiple object tracking for drones
• Air-to-ground object localization for drones
• Air-to-ground object segmentation for drones
• Application of vision foundation models for drones
• Multimodal data processing for drones

Dr. Dongdong Li
Dr. Yangliu Kuai
Prof. Dr. Hongqi Fan
Prof. Dr. Gongjian Wen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• object detection 
• object tracking 
• object localization 
• object segmentation 
• vision foundation models 
• multimodal data processing