Explosion Point Localization Method Based on Infrared Imaging

The accurate measurement of explosion point coordinates is a critical technology for assessing weapon strike efficiency and damage effects in modern warfare. Traditional optical and acoustic methods often suffer from environmental interference， high equipment costs， or a lack of real-time capability. Furthermore， emerging deep learning models require vast amounts of classified training data and high-performance computing resources， which limits their deployment on resource-constrained airborne embedded platforms. To address these challenges， this study proposes an infrared image localization method that integrates ground cooperative markers. The primary objective is to leverage the strong penetration and anti-occlusion advantages of long-wave infrared imaging to achieve high-precision， real-time explosion point detection within a lightweight framework， thereby eliminating the heavy data dependency characteristic of deep learning approaches.The methodology utilized a pinhole imaging model and a specialized set of ground cooperative markers—including cross， L-shape， straight line， and dot—to establish a stable spatial coordinate system. To facilitate marker identification on an airborne embedded platform， an enhanced convex hull detection algorithm was developed. This algorithm introduced an adjacency constraint to merge points within a 2-pixel threshold， preventing the misidentification of a “cross” caused by imaging fluctuations. Additionally， a maximum distance threshold of 18 pixels was established to distinguish between “cross” and “L-shape” markers when they exhibited similar vertex counts. Characteristic points， such as the centroid of the “cross” and the right-angle vertex of the “L-shape”， were calculated using image moments and geometric side-length comparisons to serve as the origin and axis points for the coordinate system. To maintain stability during Unmanned Aerial Vehicle （UAV） rotation， a coordinate projection transformation model using vector normalization was applied. Finally， a homography matrix was solved using singular value decomposition to map detected explosion point pixels to their actual physical world coordinates.Experimental validation was conducted through both simulations and practical tests at a height of 13 meters， utilizing an infrared camera mounted on a rotary base to simulate airborne flight dynamics. In a dataset of 1，666 frames capturing various angles and interference conditions， the enhanced convex hull detection algorithm achieved a comprehensive recognition accuracy of 98.2% for the cooperative markers. This performance significantly outperformed that of traditional algorithms， which struggled particularly with the “L-shape” markers， leading to a much lower coordinate system establishment success rate of 46.3%. In simulation-based detection， the enhanced method achieved a mean absolute error of approximately 0.01 meters with a relative error of only 0.14%. Practical experiments using fireworks to simulate explosion points demonstrated that the system could maintain high precision even under the influence of wind fields and thermal fluctuations. The average measured distance for the simulated explosion point was 1 636.6 mm against a reference of 1 631.0 mm， resulting in a measurement accuracy of 0.056 meters and a relative error of 0.3%. These results indicate a substantial improvement in precision over both traditional methods and the YOLOv8 model， the latter of which was constrained by the small target nature of the markers and limited training data.The research successfully demonstrates that a refined traditional image processing approach can effectively alleviate the data-dependency issues of deep learning in range-based applications. By combining specialized ground markers with an enhanced convex hull detection mechanism， the method enables reliable UAV localization and stable spatial coordinate construction in a simple， embedded-friendly model. The experimental outcomes confirm that the system is robust against camera rotation and background interference， providing a reliable technical foundation for the accurate measurement of explosion positions. While the current resolution of infrared cameras and the assumption of a two-dimensional terrain pose limitations， future research will focus on improving accuracy with higher-resolution sensors and testing the algorithm's stability on a dedicated fixed-wing UAV platform to account for real-world flight jitters and complex terrain undulations.