TensorFlow has made the task of object detection much easier and more efficient

Over the past few decades or so, there have been several news articles about how AI technology has revolutionized the infrastructure of modern businesses. Not just modern businesses, AI has completely transformed our personal and professional lives. Every day, more and more service-based and product-based companies are deploying artificial intelligence-powered devices for resource management, revenue management, boosting customer engagement, and enhancing customer loyalty. Experts believe that the biggest reason why AI applications are coming to fruition is due to their exponential contributions in computing power and the generation of data. And with the recent developments in advanced technologies like deep learning, computer vision, and IoT, critical tasks like object detection have also become easier than it was ever imagined before. Object detection is a computer vision technique that helps in detecting, locating, and tracking objects and images or videos. Recent revelations on this technique have disclosed that TensorFlow has emerged to be an effective tool in object detection and related tasks. Several tech professionals are now rooting for object detection with Tensorflow for its efficiency and accuracy.

TensorFlow’s object detection API is an open-source framework built on top of TensorFlow that makes it easy for professionals to construct, train, and deploy object detection models. But before understanding how object detection works with TensorFlow, let’s dive in deep into the individual significance and function of object detection and TensorFlow.

What is object detection?

Object detection is a computer vision technique in which a software system can detect, locate, and trace the object from a given image or video. The technology is capable of finding real-world objects like television, flowers, bikes, cars, and can also identify human faces and attributes instill videos and images, and this is one of the most special attributes of this technology. It identifies the class of objects and their location-specific coordinates in a given image or video.

The ability to locate the object inside an image or a video defines the algorithm’s ability to detect objects. Face detection is also a crucial form of object detection that is extensively used for security purposes. These algorithms might be pre-trained or can be trained from the scratch. In most use cases, professionals use pre-trained machines from pre-trained models and then fine-tune them as per the user’s requirements and for different other use cases.

There are several applications of object detection for industrial and business purposes, such as facial recognition, industrial quality check, self-driving cars, security, and object detection workflow, to name a few. In tasks like people counting, object detection is used for analyzing store performance and crowd statistics during festivals. It is a crucial use case since measuring crowd gathering has become a critical phenomenon in this Covid era.

What is TensorFlow?

TensorFlow is an open-source library for numerical computation and large-scale machine learning that eases Google Brain TensorFlow, the process of acquiring data, training models, serving predictions, and refining future results. Also, Tensors are just multidimensional arrays, which are an extension of 2-dimensional tables to data with a higher dimension. There are several features of TensorFlow that make it accurate for deploying technologies like deep learning.

Object Detection with TensorFlow

TensorFlow Object Detection API is the tool that conducts efficient and swift object detection. It is an open-source framework that blends in machine learning and deep learning models and algorithms. It uses Python as a convenient front-end and runs it efficiently after optimizing it with C++. The platform allows developers to create a graph of computations to perform.

There are several steps involved in detecting objects in real-time using this platform. But with the help of the OpenCV software, professionals can do it efficiently, instantly, and accurately.

Currently, TensorFlow is a popular software library. There are several real-world applications of deep learning that make TensorFlow popular. Apart from that being an open-source library for deep learning and machine learning, the platform plays the role of a text-based application, image recognition, voice search, and many more.

Recently, object detection has become a critical task for various reasons. The development of object detecting AI models in different commercially beneficial domains helped automate production lines, produce self-driving cars, assisting referees in sports, manufacturing lines, and other smaller projects. And deploying this technology will only emerge as a growing trend, with no signs of slowing down.