Object classification and localization are fundamental tasks in computer vision, and OpenCV provides robust support for deploying deep learning models. This article covers how to load pre-trained models, perform inference, and draw bounding boxes for localized objects. It uses popular architectures like YOLO or SSD, demonstrating real-time capabilities. For developers building applications such as surveillance systems, autonomous vehicles, or image analysis tools, this guide offers a solid foundation. The content is moderately technical, assuming familiarity with Python and basic deep learning concepts. While the tutorial is specific, the underlying techniques are widely applicable, making this a valuable evergreen resource.
A practical guide to using deep learning models in OpenCV for object classification and localization, valuable for computer vision developers.