What is object detection?

Object detection is a computer vision problem that detects visual items of specific kinds (animals, humans) in digital pictures like photographs or video frames. The purpose of object detection is to create computational models that answer the most basic question that computer vision applications have: “What things are there and where are they?”

Detection and computer vision

One of the most fundamental challenges in CV (computer vision) is object detection. CV tasks, including object tracking, instance segmentation, and more, are built on top of it. People counting, face, pose and text detection, and number-plate identification are examples of specific object detection applications.

  • The fast advancements of deep learning algorithms have substantially boosted the pace of object detection in recent years.

The performance of trackers and detectors has substantially increased because of deep learning networks and GPU computing capacity, resulting in significant improvements in object detection.

Machine learning (ML) is a subfield of artificial intelligence (AI) that entails learning patterns from examples or sample data as the machine accesses and learns from it.

How does it work?

Traditional image processing techniques or deep learning networks may both be used to detect objects.

  • Image processing techniques are often unsupervised and do not require prior data for training.

Advantages: As a result, such jobs do not need annotated photos, where people manually tag data (for supervised training).

Disadvantages: These approaches are limited to a variety of situations, including complicated scenarios, illumination, shadows, and occlusion.

  • The majority of deep learning approaches rely on supervised training. The compute capacity of GPUs, which is quickly expanding year after year, limits performance.

Advantages: Deep learning object recognition is far more resistant to occlusion, complicated situations, and difficult lighting.

Disadvantages: A large amount of training data is required; picture annotation is a time-consuming and expensive operation. A tiny dataset, for example, is labeling 500’000 photos to train a bespoke DL object identification algorithm. Many benchmark datasets make labeled data available.

Two-stage detectors

One-stage and two-stage object detectors are the two primary types of state-of-the-art object detection systems. Object detectors based on deep learning extract features from the input picture or video frame in general. Two tasks are solved by an object detector:

  1. Find an arbitrary number of items,
  2. Classify each one and use a bounding box to estimate its size.

You may divide those chores into two parts to make the procedure easier. Other approaches (single-stage detectors) integrate both jobs into one phase to obtain better performance at the expense of accuracy.

In two-stage object detectors, deep features are utilized to suggest approximate object areas before they are used for classification and bounding box regression for the object candidate.

  • Object region proposal using traditional Computer Vision algorithms or deep networks is followed by object categorization using bounding-box regression based on characteristics retrieved from the suggested region.
  • The maximum detection accuracy is achieved by two-stage algorithms, although they are often slower. The performance is not as excellent as one-stage detectors due to the multiple inference stages per picture.
  • RCNN (Region convolutional neural network) is a two-stage detector with Mask R-CNN and Faster R-CNN evolutions. The granulated RCNN is the most recent development (G-RCNN).
  • Object detectors with two stages first locate an area of interest, which is then clipped and used for categorization. Cropping is a non-differentiable process, which means multi-stage detectors are typically not completely trainable.

Final thoughts

One of the most fundamental and difficult challenges in computer vision is object detection. It has gotten a lot of attention in recent years, thanks to the success of DL algorithms, which now dominate state-of-the-art detection approaches.