Object detection is associated with computer vision and describes a system that can identify the presence of any desired body or object in an image. The output of an object detection process is an image bounded with boxes around the object we want to detect and describe the name of the object. This is one of the most powerful machine learning algorithms.
Autonomous driving cars have an embedded system that can perform object detection in real-time. For instance, when the autonomous car’s system detects a human-shaped body crossing the road the car stops several feet before coming in contact with the human body.
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Hardware and Software used:
First, install the following dependencies via pip
pip3 install TensorFlow
pip3 install OpenCV-python
pip3 install keras
pip3 imageai –upgrade
Download RetinaNet file that will be used for image detection
Now when all the dependencies are installed, now create the necessary folders. For now, we need these following folders:
1. Object Detection : root folder
2. Models : pre-trained models
3. Input : stores image files on which we want to do object detection
4. Output : stores images with detected objects
Object detection is a fascinating field in machine learning, it is used in research purposes also. Object detection is the most common task of computer vision. Some applications of object detection are facial recognition, this can also be used to count people for crowd statistics, also used to identify products or to check the quality of a product. Nowadays every smartphone comes with a feature that is called face unlock, object detection plays a very crucial role in this technology.
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