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Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network
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Nakagami-m Fading Detection with Eigen Value Spectrum Algorithms
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Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement
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Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm
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A Review on Meshing Techniques in Biomedicine
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EL DAPP - An Electricity Meter Tracking Decentralized Application
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SMART STREET SYSTEM WITH IOT BASED STREET LIGHT OPERATION AND PARKING APPLICATION
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ENERGY AND POWER EFFICIENT SYSTEM ON CHIP WITH NANOSHEET FET
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Abstractive Summarization System
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A Review on Meshing Techniques in Biomedicine
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MIMO BASED HIGH SPEED OPTICAL FIBER COMMUNICATION SYSTEM
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Industrial Quality Prediction System through Data Mining Algorithm
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Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement
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Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm
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VIRTUAL REALITY SIMULATION AS THERAPY FOR POSTTRAUMATIC STRESS DISORDER (PTSD)
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Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network
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Volume - 6 | Issue - 1 | march 2024
Published
03 April, 2024
This research proposes implementing YOLOv4, a real-time object detection system that can accurately and efficiently recognize objects in photos and videos. The goals include creating the YOLOv4 architecture, generating datasets, training the model, evaluating its performance, deploying it in real-world applications, and providing extensive documentation. Deep learning frameworks such as TensorFlow or PyTorch are used in the implementation, along with advanced techniques such as transfer learning and data augmentation. By curating annotated datasets and refining training techniques, the model hopes to attain high accuracy, precision, and recall in object detection tasks. Performance analysis will compare the model's outcomes to those of cutting-edge systems, assessing its strengths and weaknesses. The deployment phase involves integrating the model into existing systems or creating separate applications for real-world scenarios such as pedestrian and vehicle detection. Comprehensive documentation and a user guide will help developers and users make the best use of the trained model. Overall, this research intends to demonstrate YOLOv4's usefulness and feasibility in developing computer vision technology and supporting the creation of intelligent systems with real-time object identification capabilities.
KeywordsYOLOv4 Object detection TensorFlow Deep Learning Algorithm
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