What is object detection?
Machine Learning is a powerful, but often misunderstood technology. It’s best known for its applications in the computer vision field, but can be applied to any industry. One of the more common uses is for automatic pedestrian and car detection in footage from surveillance systems and factory video feeds.
Object detection is a deep learning technique for detecting the location of objects in an image. This technology can be used to identify pedestrians, vehicles, and other objects from images and videos. Object detection is considered to be one of the most important technologies for self-driving cars because it helps them detect other cars, pedestrians, cyclists, etc.
Detecting the location, scale, or extent of objects in an image is simple. Each detection step is reported with some form of detail that includes the location, size and shape. In other situations, the information is more detailed and contains the parameters of a linear or nonlinear transformation.
For instance, a face detector, an object detection application, is able to calculate the eye, nose, and mouth locations in addition to the bounding area of the face.
Object detection is done by taking a set of images and training the AI to single out certain aspects of it. For example, if you are looking at rigid objects only one image may be necessary but for more complex things, like human faces, many examples will be needed.
Categories of object detection models
Object detection models are categorized into three types. These are:
- Single-stage detectors
- Multi-stage detectors
- Tracking detectors.
The single stage detectors detect a single object in an image while the multi stage detectors detect more than one object at a time. Tracking detectors don’t actually detect objects themselves they track them in the video frame. The primary advantage of these detectors is they do not use a filter and are less affected by image noise. Another advantage is that these detectors have no dead zones, meaning they can track objects in any orientation. The disadvantage of this type of detector is the frame rate will be lower than other types of detectors due to the amount of computation required for tracking.
Objects detection has a wide range of applications like video surveillance, autonomous vehicles, and computer vision, medical image analysis, and human-computer interaction.
Face detection is a great use case for object detection systems. Face detection has been deployed in numerous practical scenarios, such as digital cameras that can auto-focus or even detect when someone is blinking.
Which algorithm is used for object detection?
Object detection algorithms include R-CNN (also known as Region-based Convolutional Neural Networks), Fast R-CNN, and YOLO (you only look once). All the algorithms are in the family of the R-CNN and the Fast R-CNN.R-CNN detects objects in images by first finding the regions of the image that contain similar features (by means of a convolutional neural network). Then, those regions are compared to each other to find the most likely object. Fast R-CNN is an improved version of R-CNN because it can detect objects at speeds up to 10 times faster than R-CNN and still produce comparable results.
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