Design and Optimization of an FPGA-Based Infrared Dim Small Target Detection Network Under a Sky Cloud Background

To address the challenges of infrared dim small target detection under sky cloud backgrounds on edge devices, this study proposes a lightweight sequential-differential-frame-based network (LSDF-Net) with optimization and deployment on the heterogeneous FPGA JFMQL100TAI. The network enhances detection performance through sequential-differential inputs, false-alarm-object learning, and multi-anchor assignment while reducing computational overhead through sequential-differential acceleration and convolutional pooling. Deployment efficiency is improved via image channel optimization, mixed quantization, and refining the infrared image calibration set. Experimental results indicate that the proposed network structure optimization methods reduce the hardware inference time by 15.78%. Overall, the optimized LSDF-Net achieves a recall rate of no less than 85.71% on the validation datasets and an FPS of 54.10 on the JFMQL100TAI. The proposed methods provide a reference solution for related application fields.