基于渐进时空特征融合的红外弱小目标检测

To avoid the accumulation of estimation errors from explicitly aligning multi-frame features in current infrared small-dim target detection algorithms，and to alleviate the loss of target features due to the network downsampling，a progressive spatio-temporal feature fusion network is proposed. The network utilizes a progressive temporal feature accumulation module to implicitly aggregate multi-frame information and utilizes a multi-scale spatial feature fusion module to enhance the interaction between shallow detail features and deep semantic features. Due to the scarcity of multiframe infrared dim target datasets，a highly realistic semi-synthetic dataset is constructed. Compared to the mainstream algorithms，the proposed algorithm improves the probability of detection by 4. 69% and 4. 22% on the proposed dataset and the public dataset，respectively.