Research on Infrared Target Detection Algorithm Based on Graph Globality

The current deep learning-based target detection algorithms have problems such as the network perception domain being limited, poor adaptation to scale changes, feature mismatch in feature fusion, and small datasets. Aiming at the current problems in the field of infrared target detection, a global infrared image detection method based on graph convolutional neural network is proposed. In this paper, global feature interaction module and feature pyramid module are designed. It also proposes a graph-based knowledge distillation model compression method to provide support for hardware deployment. Finally, the algorithm proposed in this paper is experimentally demonstrated, using the classical infrared small target dataset for experiments, comparing the mainstream infrared small target detection algorithms, comparing and verifying that the algorithm of this paper has an effective performance enhancement in infrared small targets in infrared targets. Design ablation experiments to verify the performance of individual modules and fusion modules[2], to prove the effectiveness and enhancement of the module. Finally, the visualization analysis facilitates the subjective evaluation by the human eye, proving the excellence of this paper's algorithm.