基于光照感知的多光谱融合行人检测方法

Multispectral pedestrian detection has been widely applied in scenarios such as intelligent security and autonomous driving. However, the accuracy and robustness of pedestrian detection still face challenges, especiallyin low-light conditions or in scenarios with occlusions. To address this issue, a novel pedestrian detection network is proposed, which is namedillumination-aware cross-spectral fusion network. Thenetwork leverages cross-attention and illumination-aware mechanisms to fully exploitmultispectral specific features, thereby improving the robustness and accuracy of pedestrian detection. To enhance feature representation between the two spectra, a cross-attention module is introduced. Additionally, an illumination-aware sub-network is proposed, which adaptively selects effective spectral feature information based on the illumination intensity variations of visible and infrared spectra, thusimproving the robustness of the detection system. Experiments areconducted on two multi-spectral pedestrian detection datasets, the KAIST dataset and the CVC-14 dataset. The experimental results demonstratethat theproposed method outperforms existing methods in terms of detection accuracy and speed. This achievementis of significant importance for enhancing the robustness and versatility of pedestrian detection models,with broad potential for practical applications.