场 景 抽 象 语 义 综 合 模 型 及 其 在 红 外 弱 小 目 标检 测 中 的 应 用

Accurate and stable detection of dim and small targets in complex，multi-task，and cross-domain environments plays a critical role in infrared early warning，search and tracking，and precision guidance. Current algorithms overly rely on manually designed strategies and prior knowledge，focusing primarily on target information extraction while insufficiently mining and utilizing scene information. This results in limited performance and inadequate environmental adaptability. To address these issues，this paper proposes a dim and small target detection method based on scene abstract semantic synthesis. First，four scene abstract semantic synthesis models are designed using cross-attention，Siamese networks，extended semantic graphs，and self-learning dual-channel methods. Next，based on these four semantic synthesis models，a dim and small target detection network based on scene semantic synthesis is designed. This network incorporates scene category semantic information into the infrared dim and small target detection process to achieve detection in various complex backgrounds. Finally，experimental results show that the proposed self-learning dual-channel semantic synthesis-based dim and small target detection algorithm achieves an accuracy of 84. 24% and a recall rate of 89. 68%.