An Object Detector is a technology or system designed to identify and localize objects within an image or video.
Object detectors use various techniques and algorithms based on machine learning and computer vision. They analyze the visual features of the input data to determine the presence and location of specific objects of interest.
There are different types of object detectors, such as:
- Traditional methods that rely on handcrafted features and rule-based algorithms.
- Deep learning-based detectors like Convolutional Neural Networks (CNNs), which have achieved significant improvements in accuracy and performance.
Some common applications of object detectors include:
- Autonomous vehicles to detect pedestrians, other vehicles, and road signs.
- Surveillance systems for identifying people or suspicious objects.
- Industrial inspection to detect defects or specific components.
For example, in a self-driving car, an object detector can quickly recognize traffic lights, other cars, and pedestrians to make safe driving decisions.
The performance of an object detector is evaluated based on metrics like accuracy, recall, and precision. Ongoing research in this field aims to improve the detection accuracy, speed, and generalization ability of object detectors to handle more complex and diverse scenarios.