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What is Object Detection? A Complete Introduction to Deep Learning

What is Object Detection? A Complete Introduction to Deep Learning

Object detection is used almost everywhere these days. Be it video surveillance, face detection, identifying and tracking objects, or spotting pedestrians. Object detection is a computer technology that helps the human race in many forms. However, first let us see what object detection actually is? Basically, this computer technology began as a several step process. It started with feature extraction and edge detection by using techniques like HOG and SIFT. The extracted image was then compared to the existing object templates at various levels. In entirety object detection is related to image processing and computer vision. Both these activities deal with the detection of instances in an image. For example, buildings, humans, cars, pedestrians, etc.) However, if we look at the current state of object detection it is easier to state that some of the highly acclaimed research domains use it for face detection too.

In addition to this, the governments around the globe are using this fantastic algorithm (in smart cities) for real-time vehicle detection and around the clock surveillance. Also, you need to understand one thing that object detection is a discipline categorized under deep learning. So, it generally involves identifying objects with the help of videos, pictures, and webcam feed. Now that you are aware of what object learning is, let us delve deeper into the concept and talk about three main algorithms (R-CNN, YOLO, SSD) used in this technology. 

R-CNN

R CNN helps in selective search. Instead of extracting all the regions from one image you can extract up to 2,000 regions ( region proposals). This also means that rather than working on all the regions you have a choice to work on lesser regions for better clarity. Additionally, R-CNN is further categorised into fast R-CNN and faster R-CNN for speedy detection of objects. So, while using R-CNN, usually, for every image scanned, there is a single sliding window via which it searches every position. It is a fairly simple solution. 

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Nonetheless, all these proposals combined together and fed into a convolutional neural network. This network, in turn, produces a 4096-dimensional feature vector. CNN plays the role of a  feature extractor and the output layer consists of the features extracted from the image. These extracted features are then fed into an SVM which classifies the presence of the object. In addition to identifying the presence of the object the algorithm also predicts the offset value. In other words, it identifies the presence of a person along with the face. For example, if the face of the person is not visible clearly, this algorithm will predict four values and then adjust the regional proposal accordingly. Region proposals are those bounding boxes that are drawn for image classification. For the second step, CNN will do the image classification in every bounding box. And finally, in the end, each of the bounding boxes will be refined by using regression. 

You Only Look Once (YOLO)

You Only Look Once, or YOLO is a different sort of object detection algorithm from the region-based algorithm like R-CNN, we read above. When all other object detection algorithm techniques use regions to produce results, the object presents the image. These kinds of networks do not scan the entire image but only some parts of them. Especially the ones with higher probability of having the object. Also, in the YOLO algorithm a single convolutional network identifies the bounding boxes along with the class probabilities of the same boxes. 

YOLO works something like this. First it splits an image into SxS grid. Then takes ‘m boundaries’ from each grid. Furthermore, for every bounding box, it will output class probability and offset value. Bounding boxes with class probabilities are then used to locate the object present within a particular image. Also, the YOLO technology works at a massive 45 FPS speed, which is faster than other systems. 

SSD or Single Shot Detectors

In the SSD algorithm, the job of object verification, localization, and classification are completed in a single advancing pass of the network. The technique for bounding box regression is known as MultiBox. This Multibox is one of the sub-components and components we have while using SSDs (Single Shot Detectors). Others will be priors and fixed prior. The base network is one of those many modules that aptly fit into the deep learning framework. The benefit of using SSD is that it has the ability to detect objects of a mixture of scales. 

Ending Note

So, this is the newest trend of artificial intelligence – object identification. Why we love the AI so much is because there is so much we still need to discover and experiment. It is serving the human race in the right way. There are some obstacles in the ways of the development of artificial intelligence but the field is evolving everyday. We are using this technology in the medical sector, at our offices, on online portals for buying dogs for protection and for better advertisement. With the rising dependency on AI, it can be concluded with assurance that artificial intelligence will grow gradually. 

What is Object Detection? A Complete Introduction to Deep Learning

Randy Meyer
Randy is child nutritionist and works with several schools around California helping parents find the right diet for their kids. He is always looking to find some new tips and healthy recipes for kids. In his free time, he likes to spend time at animal shelters and loves to look for new cafes around the city.

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