This paper explores the implementation of object detection algorithms using the CUDA Vision Workbench environment on Graphics Processing Units (GPU). The study focuses on adapting the popular Viola-Jones method, widely used in computer vision libraries like OpenCV, for execution on NVIDIA GPUs. The research addresses several key challenges in this implementation: parallelizing complex algorithms such as image integration and result grouping, optimizing memory access patterns to reduce processor idleness and improve performance, developing efficient parallel algorithms for image processing stages. The CUDA Vision Workbench library was chosen for its open-source nature, regular updates, user-friendly interface, and seamless integration with NVIDIA’s CUDA programming environment. This implementation allows real-time adjustments to the target computing system and processing algorithms, enabling visual evaluation of results based on known source images or software-generated ones. The paper presents theoretical models and experimental results comparing CPU and GPU performance for various image processing tasks. It also discusses optimization techniques like data caching in shared memory of multiprocessors and algorithms for automatic selection of the most efficient computing system for specific tasks. This research contributes to the ongoing efforts to leverage GPU capabilities for enhancing the performance of computer vision applications, particularly in object detection tasks.