Improved You Only Look Once (YOLOv5)-Based Passive Missile Detection Using Simulated Solar Blind Ultraviolet Signatures

Civilian and military aircraft are increasingly vulnerable to passive missile threats, such as short-range or within-visual-range air-to-air missiles and man-portable air defense systems. These missiles evade detection by an aircraft's radar warning receiver due to their lack of radio frequency emissions. This article presents a novel deep learning-based passive missile detection algorithm using simulated solar-blind ultraviolet (SBUV) signatures, which offer unique advantages over traditional infrared signatures. The algorithm is built on an improved You Only Look Once (YOLOv5) framework, capable of detecting and classifying UV signatures in real time from sequential image data. The architecture of YOLOv5 has been modified for improved SBUV detection by recalculation of anchor box sizes, non-maximum suppression threshold readjustment, SBUV specific data augmentations, and increased resolution in detection heads. To overcome the challenge of limited training data, we employ advanced data synthesis techniques to create realistic training datasets derived from 3D missile and aircraft combat scenario simulations in the SBUV spectrum. Performance evaluation against real-world scenarios revealed an F1-score of 95% and a mean average precision of 95% for synthetic data, compared to 88.36% and 85%, respectively, for real-world data. The algorithm achieved a detection accuracy of 92% on synthetic data and 86% on real-world data, demonstrating its reliability and robustness. Minimal performance degradation was observed under lowlight conditions, such as dusk or dawn, as well as fog. However, overcast conditions posed the most significant challenge, reducing detection accuracy from 86% to 79% and the F1-score from 88.36% to 80%.