With the rapid development and widespread application of unmanned aerial vehicle (UAV) remote sensing, the block adjustment of large-scale UAV images has become an important research content of remote sensing image processing. To solve the problem of computer memory overhead and time consumption in large-scale UAV image block adjustment, a fast and efficient solution is proposed. The traditional block adjustment procedure is optimized. The matrix Schur's complement preconditioned matrix is used to reduce the number of unknowns and the matrix's condition number respectively. The conjugate gradient truncation-Newton method greatly improves the speed of the solution of the normal equations. It gives full play to the computational potential of the hardware platform and accelerates the matrix operations by parallelizing the CPU with the GPU. The experimental results show that the proposed solution can handle tens of thousands of UAV images with high efficiency.