With the advancement in modern technologies, Artificial Intelligence (AI) has made its presence felt in the market. Object detection technology is a hot topic in today’s scenario. Most big companies are making great use of face detection, still image object counting, amongst others. We all know how efficiently computer vision object detection models run on desktop and cloud services. However, in some cases these AI models would require small size devices or hardware for a mobile user. It would also provide the user with the much sort-after aspect of privacy and this is the reason why TensorFlow Lite (TF Lite) came into existence.
What Exactly is TensorFlow Lite?
TensorFlow Lite is a technology specially designed for mobile phones and smart devices by TensorFlow. As the name suggests, 'Lite' stands for lightweight. It is highly advantageous when looking at the latest technological scenario. TensorFlow allows running machine-learned models on mobile and smart devices. The TensorFlow Lite application is supported on both Android and iOS.
Detection of objects in an image like a human eye sounds unattainable. The generation demands high accuracy and generalization, which are a daunting tasks. The technology is widely used across the globe. When talking about object detection in an image, it is imperative to understand the concept a little. It refers to the identification of objects in an image, moving or still, with special technologies. TensorFlow Lite (TF Lite) is one such technology that makes things integral.
The Prime Motive
Implementing the object detection phenomenon on an appropriate mobile app comes in handy. The object detection model identifies multiple objects in an image with bounding boxes. Object detection works perfectly with the videos or moving images as well. With TensorFlowLite object detection model, it is easier to spot living from non-living objects.
Moreover, different objects are detected with different bounding boxes and dimensions. An individual can use it any smartphone or other smart devices. One can develop the app on the phone, you can track humans, car, motorbikes, plants, and many more things. The technology also helps you identify objects in the camera preview.
Object detection lets one know about the position of various objects in the image. The object detection model is trained to identify multiple classes of items placed in an image. There is no doubt that TensorFlow Lite can easily detect multiple objects in an image.
You might have heard of face recognition on Android apps, which are trending significantly all over. The same runs on TensorFlow. A face or object detection model is specifically trained to discover the existence of multiple objects or faces in the image. These apps can easily recognize objects in the video with this model.
Now, let us discuss some chief areas where object detection comes handy.
Numerous face identification apps are successfully running in the market. Also, object detection on android apps plays a crucial role in face recognition feature. Face recognition as a feature helps identify various faces in an image. It happens in a step by step process that comprises of face detection, and recognition. The internet is making great use of TensorFlow android image recognitionapps. From Facebook to Google Lens, face identification is highly popular on social media as well.
As a Safety Measure
Mobile phones these days require image recognition to lock or unlock. Object detection on android is a crucial safety measure. It comes handy in executing retinal scans at special areas. The technology is vastly growing around the world for maintaining security at specific places. TensorFlow Lite has made a considerable impact on the mobile phone technology market. The mobile phone manufacturing companies install these models for maintaining the level of security.
Putting it simple, Object Detection is the capacity of the computers and robot vision system to identify the objects. But what is detected with a human eye is not what this technology can do. Researchers have been functioning continuously on matching the level of what is detected with the human eye, but certain limitations are becoming the hindrance in the success of this technology.
No matter how much advancement is experienced in this technology, it is tough to detect an object in the environment with noise and clutter. In the future, the technology has to be worked upon more aggressively to conquer all hindrances that we presently face while detecting objects.
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