Many of the companies involve object detection and recognition in many of their machine learning and computer science related projects in some or the other kind. Now, object detection has different interpretations in different domains. We localize objects in different domains. Object detection is not only concerned with identification and detection of object but we also localize it inside the picture(by this we mean that we include the coordinates of the “bounding box” which contains the object. We can say that detection entails classification and localization. One can learn easily learn YOLO. We have algorithms for object detection. Any type of problem - any sort it may be is solved using Deep Learning. YOLO is the first option that comes into mind when so ever we are talking about Deep Learning. YOLO can be associated with Deep Learning CNN(Convolutional Neural Network). There are several implementations of YOLO.

YOLO stands for You Only Look Once. - it uses deep learning and Convolutional neural networks(CNN) for object detection to get the bounding box. As the name YOLO it needs to see the image only once. The above mentioned statement is the reason why YOLO is the fastest network. YOLO is capable of detecting objects in real time.

Detection is carried out by dividing theimage into grid of SxS.Here, each cell predicts N probable bounding boxes and also the extent of certainty(or probability) of each of the cells. Vast majority of the boxes is going to have low probability, algorithm deletes boxes that are below a specified a threshold or a certain minimum certainty or probability.

There are three possible implementation of YOLO,each technique has its own set of advantages and disadvantages :

1.Darknet: Darknet is written in C with CUDA language and it supports GPU computation. Darknet is complete neural network and it can be used for purposes different from YOLO detection. It is difficult to find answers for errors that you find.

2.Darkflow: Darkflow works upon Tensorflow. Here, we do not require GPU to train so using CPU only Darkflow is fast.

3.AB Darknet: It is for of Darknet. It is supported in Window.
SWRL :Semantic Web Rule Language(SWRL) is a language that has been proposed for thepurpose of expressing rules along with logic in the Semantic Web. It combines OWL DL and OWL Lite along with the Rule Markup Language.SWRL is as powerful as theOWL DL, but at the price of decidability and practical implementations. Rules are basically in the form of an implication froman antecedent (body) to the consequent (head). The meaning implied can be understoodas: whenso ever the conditionsarespecified in the antecedent part, then the conditions listedin the consequent must also be true.