Python Object Detection

Object detection is a critical computer vision technique that identifies and locates objects within visual data using labels and bounding boxes, and it is widely applied in areas such as autonomous driving, construction safety, and quality inspection. This blog post provides a comprehensive guide on integrating object detection into systems using Python with the Roboflow Inference package, a library that allows the deployment of computer vision models locally, on edge devices, or in the cloud, supporting tasks like object detection, segmentation, and classification. The article walks through building Python scripts for detecting objects in images and videos using the RF-DETR model, illustrating the process of loading pre-trained models, running inferences, and visualizing results with the Supervision library. Additionally, it discusses the use of fine-tuned models from Roboflow Universe to detect specific objects, such as hardhats, which go beyond standard COCO classes, and explains how to utilize tools like Roboflow Annotate and Roboflow Maestro for creating and fine-tuning models on custom datasets.