针对复杂低空环境下无人机双光载荷采集的红外与可见光图像存在空间非配准、而现有在线配准方法计算开销大且时延波动显著、难以满足机载边缘算力约束等问题，提出一种几何先验约束下的轻量化无人机非配准双光目标检测算法。首先，利用双相机刚性固联的几何先验，在离线阶段标注跨模态同名特征点，并通过RANSAC鲁棒估计可见光到红外坐标系的单应矩阵，将主导全局映射关系的估计前置至离线阶段，避免逐帧显式估计单应矩阵，并为在线融合提供宏观粗对齐先验；其次，以轻量化检测器YOLO26为基础构建四通道早期融合输入，仅扩展首层卷积以适配RGB与IR融合输入，并采用随机初始化方式初始化新增红外通道，以避免将可见光纹理先验直接迁移至红外通道；最后，设计轻量化残差对齐模块（RAM），在红外通道作为固定参考的条件下，对可见光通道实施检测任务驱动的受限残差变换，以校正单应粗对齐后的系统性残余错位。在公开数据集DVTOD上的实验结果表明，所提方法取得0.851的mAP@0.5和0.539的mAP@0.5:0.95；同时，模型参数量为9.975M，单图处理时间为2.7ms，表明所提方法能够在较低模型复杂度下取得较好的精度与效率折中。

For infrared and visible images captured by UAV visible-infrared dual-modal payloads in complex low-altitude environments, spatial misalignment often exists between the two modalities. Existing online registration methods usually suffer from high computational cost and significant latency fluctuation, making them difficult to satisfy the constraints of onboard edge computing. To address these issues, this paper proposes a lightweight geometric-prior-constrained method for UAV-based object detection with misaligned visible-infrared images. First, based on the geometric prior of rigidly coupled dual cameras, cross-modal corresponding feature points are annotated offline, and a homography matrix from the visible image coordinate system to the infrared coordinate system is robustly estimated using RANSAC. In this way, the estimation of the dominant global mapping relationship is moved to the offline stage, avoiding frame-by-frame explicit homography estimation and providing a macroscopic coarse-alignment prior for online fusion. Second, a four-channel early-fusion input is constructed based on the lightweight YOLO26 detector. Only the first convolutional layer is extended to adapt to the RGB-IR fusion input, and the newly added infrared channel is initialized randomly to avoid directly transferring visible-light texture priors to the infrared channel. Finally, a lightweight residual alignment module, namely RAM, is designed. With the infrared channel used as a fixed reference, RAM applies a detection-task-driven constrained residual transformation to the visible channels to correct systematic residual misalignment after homography-based coarse alignment. Experimental results on the public DVTOD dataset show that the proposed method achieves an mAP@0.5 of 0.851 and an mAP@0.5:0.95 of 0.539. Meanwhile, the model has 9.975M parameters and a per-image processing time of 2.7 ms, demonstrating that the proposed method achieves a favorable trade-off between accuracy and efficiency with low model complexity.