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Creating accurate Machine Learning Models which are capable of identifying and localizing multiple objects in a single image remained a core challenge in computer vision. But, with recent advancements in Deep Learning, Object Detectionapplications are easier to developthan ever before. TensorFlows Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models.

So guys, in this Object Detection Tutorial, Ill be covering the following topics:

You can go through this real-time object detection video lecture where our Deep Learning Training expert is discussing how to detect an object in real-time using TensorFlow.

This Edureka video will provide you with a detailed and comprehensive knowledge of TensorFlow Object detection and how it works. It will also provide you with the details on how to use Tensorflow to detect objects in the deep learning methods.

Object Detection is the process of finding real-world object instances like car, bike, TV, flowers, and humans in still images or Videos. Itallows for the recognition, localization, and detection of multiple objects within an image which provides us with a much better understanding of an image as a whole. It is commonly used in applications such as image retrieval, security, surveillance, and advanced driver assistance systems (ADAS).

Object Detection can be done via multiple ways:

In this Object Detection Tutorial, well focus on Deep Learning Object Detection as Tensorflow uses Deep Learning for computation.

Lets move forward with our Object Detection Tutorial and understand itsvarious applicationsin the industry.

A deep learning facial recognition system called the DeepFace has been developed by a group of researchers in the Facebook, which identifies human faces in a digital image very effectively. Google uses its own facial recognition system in Google Photos, which automatically segregates all the photos based on the person in the image. There are various components involved in Facial Recognition like the eyes, nose, mouthand the eyebrows.

Object detection can be also used for people counting, it is used for analyzing store performance or crowd statistics during festivals. These tend to be more difficult as people move out of the frame quickly.

It is a very important application,as during crowd gathering this feature can be used for multiple purposes.

Object detection is also used in industrial processes to identify products. Finding a specific object through visual inspection is a basic task that is involved in multiple industrial processes like sorting, inventory management, machining, quality management, packaging etc.

Inventory management can be very tricky as items are hard to track in real time. Automatic object counting and localization allows improving inventory accuracy.

Self-driving cars are the Future, theres no doubt in that. But the working behind it is very tricky as it combines a variety of techniques to perceive their surroundings, including radar, laser light, GPS, odometry, and computer vision.

Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and once the image sensor detects any sign of a living being in its path, it automatically stops. This happens at a very fast rate and is a big steptowards Driverless Cars.

Object Detection plays a very important role in Security. Be it face ID of Apple or the retina scan used in all the sci-fi movies.

It is also used by the government to access the security feed and match it with their existing database to find any criminals or to detect the robbers vehicle.

The applications are limitless.

Every Object Detection Algorithm has a different way of working, but they all work on the same principle.

Feature Extraction: They extract features from the input images at hands and use these features to determine the class of the image. Be it through MatLab, Open CV, Viola Jones or Deep Learning.

Now that you have understood the basic workflow of Object Detection, lets move ahead in Object Detection Tutorial and understand what Tensorflow is and what are its components?

Tensorflow is Googles Open Source Machine Learning Frameworkfor dataflow programming across a range of tasks.Nodes in the graph represent mathematical operations, while the graph edges represent the multi-dimensional data arrays (tensors) communicated between them.

Tensors are just multidimensional arrays, an extension of 2-dimensional tables to data with a higher dimension. There are many features of Tensorflow which makes it appropriate for Deep Learning. So, without wasting any time, lets see how we can implement Object Detection using Tensorflow.

After the environment is set up, you need to go to the object_detection directory and then create a new python file. You can use Spyder or Jupyter to write your code.

Next, we will download the model which is trained on the COCO dataset. COCO stands for Common Objects in Context,this dataset contains around 330K labeled images. Now the model selection is important as you need to make an important tradeoff between Speed and Accuracy. Depending upon your requirement and the system memory, the correct model must be selected.

Inside models>research>object_detection>g3doc>detection_model_zoo contains all the models with different speed and accuracy(mAP).

Now, lets move ahead in our Object Detection Tutorial and see how we can detect objects in Live Video Feed.

For this Demo, we will use the same code, but well do a few tweakings. Here we are going to use OpenCV and the camera Module to use the live feed of the webcam to detect objects.

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This code will use OpenCV that will, in turn, use the camera object initialized earlier to open a new window named Object_Detection of the size 800600. It will wait for 25 milliseconds for the camera to show imagesotherwise, it will close the window.

Now with this, we come to an end to this Object Detection Tutorial. I Hope you guys enjoyed this article and understood the power of Tensorflow, and how easy it is to detect objects in images and video feed. So, if you have read this, you are no longer a newbie to Object Detection and TensorFlow. Try out these examples and let me know if there are any challenges you are facing while deploying the code.

Now that you have understood the basics ofObject Detection, check out the AI and Deep Learning With Tensorflowby Edureka,a trusted online learning companywith a network of more than250,000satisfied learnersspread acrossthe globe. This Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn.

Got a question for us? Please mention it in the comments section of Object Detection Tutorial and we will get back to you.

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