DTPFN: End-to-End Multi-Source Fusion Detection Network for Robust Performance in Harsh Environments

For fields with high requirements under harsh environments, such as military strikes, defense, and disaster rescue, the performance of traditional single-source image detection methods drops significantly. Moreover, the multi-source image fusion methods are structurally redundant for detection tasks and have a limited scope of application. In this paper, we put forward an end-to-end multi-source fusion detection network. By utilizing the Dual-Source Three-Branch Parallel Fusion Network (DTPFN), we achieve full fusion of multi-source features and obtain excellent performance in various environments. Our experimental findings demonstrate that even in the presence of interference within a single source, our proposed approach can still retain a relatively high level of detection accuracy. Additionally, it exhibits remarkable performance in terms of both resource consumption and detection speed. Through ablation experiments, we further verify the effectiveness of each module and especially prove the crucial impact of the complementary features of DTPFN on detection performance. The DTPFN can not only fully fuse the features but also be conveniently extended, exhibiting strong potential and holding broad prospects for practical applications.