【摘要】 针对多光谱行人检测中双模态特征融合不充分、特征融合质量低的问题，提出一种基于多阶段交叉信息融合的多光谱行人检测算法。算法首先通过双流骨干网络分别对可见光图像和红外图像进行特征提取；设计交叉信息融合模块并多阶段嵌入双流骨干网络中引导双模态特征融合，实现双模态特征信息的充分融合；引入条件卷积对融合后的特征信息进行动态处理，改善融合信息的质量，最终提升算法的检测性能。实验结果表明，算法的漏检率仅为10.41%,较原算法降低了10%,显著提升了算法的检测性能。更多还原

【Abstract】 To solve the problems of insufficient bimodal feature fusion and low quality of feature fusion in multispectral pedestrian detection, a multispectral pedestrian detection algorithm based on multistage cross information fusion is proposed. Firstly, the algorithm extracts the features of visible and infrared images through the dual stream backbone network; The cross information fusion module is designed and embedded in the dual stream backbone network in multiple stages to guide the bimodal feature fusion, so as to achieve full fusion of bimodal feature information; Conditional convolution is introduced to dynamically process the fused feature information to improve the quality of the fused information and ultimately improve the detection performance of the algorithm. Experimental results show that the missing rate of the algorithm is only 10.41%, which is 10% lower than the original algorithm, and the detection performance of the algorithm is significantly improved.更多还原