DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition

• Published in: SN computer science Vol. 5; no. 6; p. 770
• Main Authors: Shianios, Demetris, Kolios, Panayiotis S., Kyrkou, Christos
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 08.08.2024; Springer Nature B.V
• Subjects: Artificial intelligence; Artificial neural networks; Building failures; Classification; Computer Analysis of Images and Patterns in the Deep Learning Era; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Context; Data collection; Data Structures and Information Theory; Datasets; Deep learning; Disasters; Earthquakes; Efficiency; Embedded systems; Emergency preparedness; Feature extraction; Floods; Forest & brush fires; Frames per second; Information Systems and Communication Service; Labels; Original Research; Pattern Recognition and Graphics; Performance evaluation; R&D; Real time; Research & development; Satellites; Seismic engineering; Social networks; Software Engineering/Programming and Operating Systems; Test sets; Unmanned aerial vehicles; Vision; Convolutional Neural Networks; Vision Transformers; Natural disaster recognition; UAV (Unmanned Aerial Vehicle); Multi-label classification; Image classification
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-03066-y

The integration of Unmanned Aerial Vehicles (UAVs) with artificial intelligence (AI) models for aerial imagery processing in disaster assessment, necessitates models that demonstrate exceptional accuracy, computational efficiency, and real-time processing capabilities. Traditionally Convolutional Neural Networks (CNNs), demonstrate efficiency in local feature extraction but are limited by their potential for global context interpretation. On the other hand, Vision Transformers (ViTs) show promise for improved global context interpretation through the use of attention mechanisms, although they still remain underinvestigated in UAV-based disaster response applications. Bridging this research gap, we introduce DiRecNetV2, an improved hybrid model that utilizes convolutional and transformer layers. It merges the inductive biases of CNNs for robust feature extraction with the global context understanding of Transformers, maintaining a low computational load ideal for UAV applications. Additionally, we introduce a new, compact multi-label dataset of disasters, to set an initial benchmark for future research, exploring how models trained on single-label data perform in a multi-label test set. The study assesses lightweight CNNs and ViTs on the AIDERSv2 dataset, based on the frames per second (FPS) for efficiency and the weighted F1 scores for classification performance. DiRecNetV2 not only achieves a weighted F1 score of 0.964 on a single-label test set but also demonstrates adaptability, with a score of 0.614 on a complex multi-label test set, while functioning at 176.13 FPS on the Nvidia Orin Jetson device.