The scale-invariant feature transform (SIFT) algorithm is the representative approach for key-point detection in the field of digital image processing. A binocular distance measurement system is established herein based on the improved SIFT algorithm by using open computing language (OpenCL) parallel computing as an acceleration method, and a profound study on how to speed up the operation of SIFT algorithm is performed. First, the integral mean blur is selected to speed up the SIFT algorithm operation. OpenCL parallel computing is then used to accelerate it. The parallel optimization of the algorithm is made to be implemented on NVIDIA GPU hardware platforms. The original SIFT matching method is improved to obtain an accurate parallax. Consequently, the matching efficiency has been greatly improved. Finally, a binocular distance measurement system heterogeneous computing experimental platform is constructed. The experimental platform performs a real-time processing on the acquired images. The feasibility of parallel acceleration based on the SIFT algorithm is verified. An intermediate calculation process and the distance measurement results can be directly obtained in the system. The experimental results show that compared with the previous accelerated optimization work to the SIFT, the computational time consumption of the proposed approach is much less than that in the original method.