Multiscale self-attention for unmanned ariel vehicle-based infrared thermal images detection

Object detection and recognition in unmanned aerial vehicle-based images is critical for various applications but is often challenged by complex backgrounds, diverse object scales, densely clustered small objects, and uneven object distributions. This paper introduces a novel deep learning-based artificial intelligence framework that integrates the Multiscale Self-Attention Guidance and Feature Fusion Network with the You Only Look Once model, tailored explicitly for artificial intelligence-driven unmanned aerial vehicle-based infrared thermal image analysis. The proposed methodology offers four key advancements in the You Only Look Once architecture to enhance object detection performance. First, the Multi-Head Self-Attention Transformer module combines global and local information, enabling precise object localization while mitigating the influence of complex backgrounds. Second, the Multiscale Parallel Sampling Feature Fusion module optimizes the fusion of multiscale features. Third, fine-grained shallow feature maps are integrated into the fusion process to detect densely packed small objects accurately. Lastly, the Inverse-Residual Feature Enhancement module, positioned before the detection head, enhances feature extraction for small objects. Experimental evaluations on the High Altitude Infrared Thermal Unmanned Aerial Vehicle dataset demonstrate significant improvements, achieving a Mean Average Precision of 95.1%, Recall of 92.0%, and F1-Score of 91.0%. The framework’s robustness is further validated on the Wildland-fire Infrared Thermal Unmanned Aerial System dataset, achieving a Mean Average Precision of 82.1%, Recall of 88.0%, and F1-Score of 82.0%. Comparative analyses with state-of-the-art methods confirm its superiority and offer a scalable artificial intelligence-driven solution for unmanned aerial vehicle applications, advancing object detection capabilities in critical scenarios.