Object Detection

Object detection helps computers identify what appears in an image and where each item is located.

Rather than assigning a single label, such as cat, an object detection model produces a list of objects along with bounding boxes that show their positions. This capability enables machines to interpret complex, real-world scenes, including traffic intersections, grocery store shelves, medical scans, and factory assembly lines.

From classical computer vision to deep learning

Before deep learning, object detection relied on manually crafted features.

Techniques such as Haar Cascades and Histogram of Oriented Gradients (HOG) searched for edges, textures, and patterns defined by humans. While these methods were fast, they were fragile—their performance dropped sharply when objects appeared under different lighting conditions, angles, or backgrounds. CNN-based detectors transformed this approach. Convolutional layers automatically learn relevant features directly from data, shifting image understanding from manual feature engineering to end-to-end learning.

Academic research and industry quickly converged on two families of architectures:

• Two-stage models (R-CNN → Fast R-CNN → Faster R-CNN):

  • They first generate region proposals, then classify them. This two-step process makes them highly accurate and reliable for medical imaging, satellite data, and scientific analysis, where missing an object can be costly.

• One-stage models (SSD, YOLO):

  • They skip proposals and predict boxes + labels in one pass. This makes them fast and real-time, ideal for drones, robotics, traffic cameras, and mobile apps.

This era, which spanned from 2015 to 2020, remains the backbone of many enterprise systems today.

The DETR revolution

In 2020, DEtection TRansformer (DETR) introduced a radical ...