TensorFlow Object Detection
Object detection is a process of discovering real-world object detail in images or videos such as cars or bikes, TVs, flowers, and humans. It allows identification, localization, and identification of multiple objects within an image, giving us a better understanding of an image. It is used in applications such as image retrieval, security, surveillance, and the Advanced Driver Assistance System (ADAS).
Applications of Object Detection
Facial Recognition:
A deep learning facial recognition system called “Deep Face” has been developed by a group of researchers on Facebook, which very effectively identifies the human face in a digital image. Google Photos, which automatically separates all pictures based on the person in the picture. Many components are involved in facial recognition, such as face, nose, mouth, and eyebrow.
Industrial Quality Check:
Object detection is also used in the industrial process to identify products. Finding a specific object by visual inspection is an essential task that is involved in multiple industrial processes like inventory management, machining, quality management, packaging, sorting, etc.
Inventory management is very tricky as items are hard to track in real-time. Automatic localization and object counting allows for improving inventory accuracy.
Self-Driving Cars:
Self-driving cars are the future cars. But the working backside is very tricky like it combines a variety of techniques to perceive its atmosphere, including radar, laser light, GPS, odometry, and computer vision.
There are advanced control systems that interpret sensory information to identify appropriate navigation paths, as well as obstacles. Once ever the image sensor detects any sign of living thing in its way, it automatically stops. This happens at a rapid rate and is a big step towards Driverless cars.
People Counting:
Object detection can be used for people counting, and it is used for analyzing store performance or crowd figures during festivals. It tends to be more difficult as people move out of the frame quickly.
It is a critical application during crowd gathering; this feature can be used for multiple purposes.
Object Detection Workflow
Every object Detection algorithm is working in different teaching, but they all work on the same principle.
Feature Extraction: They extract the features from the input images at hand and use these features to determining the class of the picture. Be it through Mat Lab, Open CV, Viola-Jones, or Deep learning.
Prerequisites
- Python
- TensorFlow
- Tensorboard
- Protobuf v3.4 or above
Environment set-up
Now to download TensorFlow and TensorFlow GPU, we can use pip or conda commands which we have at the start.
Complete command
Output-
