MDDFA-Net: Multi-Scale Dynamic Feature Extraction from Drone-Acquired Thermal Infrared Imagery

UAV infrared sensor technology plays an irreplaceable role in various fields. High-altitude infrared images present significant challenges for feature extraction due to their uniform texture and color, fragile and variable edge information, numerous background interference factors, and low pixel occupancy of small targets such as humans, bicycles, and diverse vehicles. In this paper, we propose a Multi-scale Dual-Branch Dynamic Feature Aggregation Network (MDDFA-Net) specifically designed to address these challenges in UAV infrared image processing. Firstly, a multi-scale dual-branch structure is employed to extract multi-level and edge feature information, which is crucial for detecting small targets in complex backgrounds. Subsequently, features at three different scales are fed into an Adaptive Feature Fusion Module for feature attention-weighted fusion, effectively filtering out background interference. Finally, the Multi-Scale Feature Enhancement and Fusion Module integrates high-level and low-level features across three scales to eliminate redundant information and enhance target detection accuracy. We conducted comprehensive experiments using the HIT-UAV dataset, which is characterized by its diversity and complexity, particularly in capturing small targets in high-altitude infrared images. Our method outperforms various state-of-the-art (SOTA) models across multiple evaluation metrics and also demonstrates strong inference speed capabilities across different devices, thereby proving the advantages of this approach in UAV infrared sensor image processing, especially for multi-scale small target detection.