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Object detection: This is the process of finding and classifying variables objects in an image or in video. Algorithms are fed special features which determine which class a particular object belongs. Illustrating with the example of circle, special features includes round shape. This may be considered one of hot burning research which comes under computer vision or image processing. In broader sense it is the combination of localization and classification.
Models/ Algorithms for achieving the same: To achieve the above various deep learning models can be used. You need Titan X GPU for training purpose.
1.) Convolutional neural network
2.) You Only Look Once
3.) Single Shot Detectors
Step1: Images are Pre-Processed to normalize contrast and brightness effects. If dealing with colored images conversion to LAB color space may help in getting better results. Images should be resized to fixed size so that next step of feature extraction will get implemented with ease.
Step 2: Extraction of Features: This is one of the most important steps in any machine learning algorithm. Images contain many irrelevant information which is not needed in the next step or training phase so only those features are retained which are useful. Various algorithms include Haar-like features, Histogram of oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) etc.
Step3: Images are trained using appropriate algorithm which depends upon nature of target.
Step4: Step 3 requires GPU for training. More is the training, more learning of the algorithm will take place and hence more accurate prediction will be the outcome.
Step5: Real time images are provided to the model which produces the output. Accuracy of the result depends upon preprocessing, feature extraction and size of training images.
1.) Boundary creation for object localization.
2.) Real time model for object detection. Communication with the client or end user will specify the usage of CPU or GPU as later is the expensive technology.
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