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A-13 Iomp-1.pptx
- 1. INDUSTRY ORIENTED MINI PROJECT PRESENTATION ON AI based Detection to find the different objects with in the part of image BATCH NO : A - 13 UNDER THE ESTEEMED GUIDANCE OF : MR. G RAVI, ASSISTANT PROFESSOR. NAME OF PROJECT COORDINATOR: PROJECT BATCH STUDENTS NAMES & ROLL NUMBERS 20BK5A0416 – B. JAYENDRANATH 20BK5A0404 – A. ISAAC PAUL 20BK5A0413 – B. PRATHYUSHA
- 2. ABSTRACT In this project we will see deeper and look at various algorithms that can be used for object detection. We will start with the algorithms belonging to RCNN family, i.e. RCNN, Fast RCNN and Faster RCNN. In the upcoming article of this series, we will cover more advanced algorithms like YOLO, SSD, etc. Main use of this project is to detect the objects at any type and covert the object into speech. We can detect with in seconds using YOLO with RCNN. using this we can detect images, create images. Yolo gives better output and taking less speed than others.
- 3. INTRODUCTION • The algorithm applies a neural network to an entire image. The network divides the image into an S x S grid and comes up with bounding boxes, which are boxes drawn around images and predicted probabilities for each of these regions. • The method used to come up with these probabilities is logistic regression. The bounding boxes are weighted by the associated probabilities. For class prediction, independent logistic classifiers are used. • In this article, demonstrate how to implement the YOLO algorithm with a pre trained model. • First, we would need to install DarkNet. it is a neural network framework that is open source.
- 4. EXISTING SYSTEM • Edge detection. • Morphological filters. • Svm classification. DISADVANTAGES: • Not a real time application. • Information of objects is very less
- 5. PROPOSED SYSTEM • Caffe model data set(Darknet) • Deep learning classification • Blob detection ADVANTAGES: • Maximum accuracy in classification • Real time achievement • Machine based prediction APPLICATIONS: • Commercial applications • Forensic lab • Face recognition
- 7. FLOW CHART Input video Pre-Processing Blob detection YOLO Algorithm Resize filteration Colour covr
- 8. RESULTS AND DISCUSSION The important discussion in our project is detect objects using yolo with rcnn. So, take yolo and detect every object with in less time and get more accuracy. These are used for the face recognition
- 9. CONCLUSION AND FUTURE SCOPE We introduce YOLO, a unified model for object detection. Our model is simple to construct and can be trained directly on full images. Unlike classifier-based approaches, YOLO is trained on a loss function that directly corresponds to detection performance and the entire model is trained jointly. Fast YOLO is the fastest general-purpose object detector in the literature and YOLO pushes the state-of-the-art in real-time object detection. YOLO also generalizes well to new domains making it ideal for applications that rely on fast, robust object detection. then detected image is converted into text,after that text to speech conversion occure.