A Robust Small Target Recognition Algorithm in Complex Backgrounds Based on Multichannel Image Fusion and Self-Supervised Learning

Small target recognition in complex backgrounds presents significant challenges in fields such as intelligent security, remote sensing, and medical image diagnostics.Diverse textures, strong noise, and varying illumination conditions in complex scenes often lead to blurred features and low contrast for small targets.Traditional recognition algorithms struggle to effectively extract key features under these conditions, resulting in insufficient accuracy and robustness.Existing multichannel image fusion methods-such as weighted averaging or wavelet transforms-either ignore the correlation of feature spaces and semantic information or rely on specific parameters with high computational complexity, limiting their ability to highlight fine target details.Meanwhile, supervised learning-based recognition approaches heavily depend on large amounts of labeled data and exhibit poor generalization in unfamiliar complex environments.To address these issues, this paper proposes a robust recognition algorithm based on multichannel image fusion and selfsupervised learning.The main contributions include: (1) the design of a multichannel image fusion method tailored for small targets, which enhances target-background contrast by leveraging the complementary characteristics of different imaging channels; and (2) the development of a self-supervised learning framework that automatically learns generalizable feature representations from unlabeled data, reducing the reliance on manual annotations and improving model generalization.This research overcomes the limitations of traditional methods regarding label dependency and adaptability to complex backgrounds, offering a novel technical approach for small target recognition.Theoretically, it enriches the fields of computer vision and pattern recognition; practically, it contributes to enhancing the intelligence level of relevant application domains.