Semantic-Aware Resource Allocation for Infrared Small-Target Detection in UAV Communication Systems

In resource-constrained unmanned aerial vehicle (UAV) infrared image transmission, infrared small targets usually occupy only a small number of pixels, while conventional uniform resource allocation strategies fail to consider the semantic differences among image regions, resulting in inefficient resource utilization and loss of target-related information. To address this issue, this paper proposes a semantic-aware resource allocation method for infrared small-target detection. First, infrared images are divided into multiple grid cells, and the semantic importance of each cell is estimated based on preliminary detection results. Then, the cells are classified into different semantic levels, followed by differentiated bit allocation and region-wise reconstruction. Finally, a joint optimization problem of grid partitioning and resource allocation parameters is formulated and solved by the covariance matrix adaptation evolution strategy (CMA-ES) to optimize both detection performance and image reconstruction quality. Experimental results demonstrate that the proposed method achieves mIoU values of 0.6106 and 0.7445 at available bit rates of 12.5% and 50%, respectively, showing significant improvements over comparison methods. Moreover, CMA-ES obtains stable optimization results with fewer evaluations and provides a favorable balance between detection accuracy and reconstruction quality. These results indicate that the proposed method can effectively preserve target-related semantic information under limited communication resources, providing an effective solution for efficient UAV infrared image transmission and reliable small-target detection.