Computer vision is one of the important fields of artificial intelligence. Computer vision is the ability of the computers and software systems to recognize and understand the images and the objects in the images. Computer vision comprises of many aspects like object detection, image recognition, image generation, image super-resolution etc. Object detection is the most useful aspect of computer vision. This is because of a huge number of real-life applications.
In this article, we will discuss the modern object detection, what are the challenges faced by software developers and what are the solutions to the problem for high accuracy object detection.
Object detection is the capability of the computer and software systems to locate the objects in the image/scene and identify each object. Object detection has many real-life applications. It is used for face detection, vehicle detection, web images, pedestrian counting, security systems, driverless cars etc. Apart from this, there are many applications where object detection can be used. There are many other real-life applications of the object detection.
Like any other computer technology, a wide range of real-life applications can emerge from the efforts of computer programmers and software developers. But implementing the modern object detection model and building a new application based on these models is not as easy as it seems.
In the earlier times, the implementation of object detection involved the use of classical algorithms like the one supported in OpenCV, which is a popular computer vision library. However, these classical algorithms could not achieve enough performance to work under different conditions.
Deep Learning algorithm bought a revolution in object detection. The adoption of deep learning techniques brought into existence the highly accurate object detection algorithms as well as methods, for example, CNN, Fast-RCNN, Faster-RCNN, RetinaNet and fast yet highly accurate algorithms like SSD and YOLO. All these algorithms are based on deep learning as well as machine learning. These algorithms require a lot of mathematical as well as deep learning understanding.
There are millions of expert computer programmers and developers who want to create new products that use object detection. But this technology is out of the reach of many software developers due to the complication involved in the algorithm.
Not long before, deep learning was very slow and not feasible for practical applications. To speed up the process, faster CPUs were also needed because of the training involved. GPUs provide the boost to speed that was needed for the better accuracy in less time.
Deep Learning with transfer learning
Deep learning neural network also provides a big step towards solving the problems such as computer vision. However, developers are having the main aim of leveraging the work of others. Writing deep learning and neural network code from the scratch would take a long time and it may not function with great efficiency. But with the help of frameworks available and reusable models, deep learning has become easier to implement.
With the help of frameworks and models, a new artificial intelligence developer can also develop image classification and object detection. Not long ago it was not possible. Today, you can use transfer learning i.e. use an existing image recognition model and train it with your dataset.
Making it Accessible
The next thing is to make frameworks, models and the GPUs to work together. Developers who have to deliver a specific app don't have the time to learn and build the AI platforms and be a data scientist. Here simplification is needed and with the help of the platforms like PowerVisionAI, developers can focus on implementing the app rather than implementing the platform from scratch. Loading the datasets of images and labeling the objects is a task that can be done by anyone. There is no need to write the code.
Object Detection versus Image Classification
Object detection versus image classification is the key decision when it comes to computer vision technology. For example, for example, if you have a picture of an animal, do you want to know whether it is a dog or not? Or your requirement is to locate the dogs in the picture and count them? Looking at the picture and choosing a label is image classification. But if you want to locate the dog in the picture, then it is object detection.
Training a model for the image classification requires examples of datasets for each label. For example in PowerAI, you would select a dog label and draw a bounding box around each dog. After your dataset has enough images with ample labeling, you can train a model. If your dataset is not accurate enough, you can add more data and train it.
The newly developed algorithms will make it possible to easily expand the scope of computer vision technology and real-time object detection. Using the image regions based on deep learning, only a single image is required to carry out the identification process for carrying out an identification map. This massively reduces the number of calculations and makes the real-time object detection easier using artificial intelligence applications.
Webtunix is an artificial intelligence company that has successfully provided solutions in the computer vision technology and real-time object detection.