The Initial Attitude Estimation of an Electromagnetic Projectile in the High-Temperature Flow Field Based on Mask R-CNN and the Multi-Constraints Genetic Algorithm

During the launching process of electromagnetic projectiles, radiated noise, smoke, and debris will interfere with the line of sight and affect the accuracy of initial attitude estimation. To address this issue, an enhanced method that integrates Mask R-CNN and a multi-constraint genetic algorithm was proposed. First, Mask R-CNN was utilized to perform pixel-level edge segmentation of the original image, followed by the Canny algorithm to extract the edge image. This edge image was then processed using the line segment detector (LSD) algorithm to identify the main structural components, characterized by line segments. An enhanced genetic algorithm was employed to restore the occluded edge image. A fitness function, constructed with Hamming distance (HD) constraints alongside initial parameter constraints defined by centroid displacement, was applied to boost convergence speed and avoid local optimization. The optimized search strategy minimized the HD constraint between the repaired stereo images to obtain accurate attitude output. An electromagnetic simulation device was utilized for the experiment. The proposed method was 13 times faster than the Structural Similarity Index (SSIM) method. In a single launch, the target with 70% occlusion was successfully recovered, achieving average deviations of 0.76°, 0.72°, and 0.44° in pitch, roll, and yaw angles, respectively.