Building a Linux-Based Object Detection System
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Object detection is easier than it used to be. We'll show you how to build an app that finds objects in medical images using the YOLOv8 model architecture.
Object detection isn't hiding in research labs anymore. A single developer or a small technical team can train a custom detection model. And all this can be done on a consumer-grade Linux machine with free and open source tools and public datasets. In this article, I'll walk you through building a complete object detection system on Linux. I'll use the YOLOv8 object detector from Ultralytics [1]. To make this project a sensible case study, I'll train the model to detect head structures in obstetric ultrasound images. I tried to make every step reproducible. By the end, you'll have a working detection pipeline that you can adapt for other uses.
Prerequisites
Before I dive into the model training and other steps, make sure the system meets the baseline requirements. Any machine running Linux (I tested it on Ubuntu 24.04 LTS), Windows, or macOS will work. A dedicated GPU accelerates training significantly but is not mandatory. I used my Core i5 CPU, but it took significant time.
You'll need Python 3.9 or later. Verify with:
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