In a recent project, I utilized YOLOv8, an object detection algorithm, along with OpenCV to develop an efficient and accurate object detection system.
The goal of the project was to create a system that could detect and identify various objects in real-time using computer vision techniques. I implemented YOLOv8, which is a state-of-the-art object detection model known for its speed and accuracy. This model is trained on a large dataset and can detect a wide range of objects, including people, vehicles, and everyday items.
I utilized OpenCV, a popular computer vision library, for image processing tasks such as image resizing, color conversion, and drawing bounding boxes around detected objects. OpenCV provides a wide range of functions and tools for image manipulation and analysis, which were instrumental in preprocessing the images for the object detection pipeline.
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