A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data
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Rohit Rastogi
Abstract
This comparison between the yolov8s.pt and yolov8x.pt YOLOv8 models is very important for real-time applications, particularly for object recognition and surveillance. Based on the results, the 95% precision and recall of the yolov8s.pt model, together with its 96% mean average precision (mAP), demonstrate the model’s usefulness in situations requiring precise and quick object recognition. This model has potential applications in a variety of security systems, supporting security protocols in high-risk areas such as airports, public areas, and high-security enterprises by assisting in the quick identification of possible threats in real-time surveillance data. Conversely, the yolov8x.pt model’s better performance - which includes an astounding 98% precision and 99% mAP - highlights its effectiveness in demanding real-time applications that need exacting accuracy. Because of its complex capabilities, the model is a great fit for use in cutting-edge applications that require quick and accurate object recognition such as autonomous driving technologies and sophisticated surveillance systems. By enabling quick detection and avoidance of possible risks or obstructions, its possible integration into autonomous cars might greatly improve road safety and advance the development of more dependable and safe autonomous driving systems.
Abstract
This comparison between the yolov8s.pt and yolov8x.pt YOLOv8 models is very important for real-time applications, particularly for object recognition and surveillance. Based on the results, the 95% precision and recall of the yolov8s.pt model, together with its 96% mean average precision (mAP), demonstrate the model’s usefulness in situations requiring precise and quick object recognition. This model has potential applications in a variety of security systems, supporting security protocols in high-risk areas such as airports, public areas, and high-security enterprises by assisting in the quick identification of possible threats in real-time surveillance data. Conversely, the yolov8x.pt model’s better performance - which includes an astounding 98% precision and 99% mAP - highlights its effectiveness in demanding real-time applications that need exacting accuracy. Because of its complex capabilities, the model is a great fit for use in cutting-edge applications that require quick and accurate object recognition such as autonomous driving technologies and sophisticated surveillance systems. By enabling quick detection and avoidance of possible risks or obstructions, its possible integration into autonomous cars might greatly improve road safety and advance the development of more dependable and safe autonomous driving systems.
Chapters in this book
- Frontmatter I
- Contents V
- Contributing authors IX
- Synchronizing neural networks, machine learning for medical diagnosis, and patient representation: looping advanced optimization strategies assisting experts for complex mechanisms behind health and disease detection 1
- The future of predictive health: evaluating the role of neural network based hybrid models in healthcare 19
- An overview of new trends on deep learning models for diabetes risk prediction 47
- A study on the detection and diagnosis of cervical cancer using machine and deep learning models 57
- Sentiments and opinions shared on social media during the COVID-19 pandemic using machine learning techniques 71
- Combining decision tree and Bayesian networks for improved predictive analytics 91
- Emerging trends in hybrid information systems modeling in artificial intelligence 115
- Hybrid approaches for improving cybersecurity and network intrusion system 153
- IoT security enhancement through blockchain solutions 167
- Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions 177
- Hybrid information systems for modeling traffic management and control 201
- Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: a synchronized approach 223
- A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data 241
- Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning 269
- Recent advancements in perfect difference networks for image recognition: a survey and analysis 307
- Image to text to speech: a web-based application using optical character recognition and speech synthesis 329
- Biomimicry and nature-inspired solutions for environmental sustainability 343
- Intelligent analysis of flowers and knowledge generation: an empirical study for agriculture 4.0 355
- Harnessing the power of hybrid models for supply chain management and optimization 407
- Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide 427
- Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide 443
- Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide 459
- Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide 473
- Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies 491
- Index 501
Chapters in this book
- Frontmatter I
- Contents V
- Contributing authors IX
- Synchronizing neural networks, machine learning for medical diagnosis, and patient representation: looping advanced optimization strategies assisting experts for complex mechanisms behind health and disease detection 1
- The future of predictive health: evaluating the role of neural network based hybrid models in healthcare 19
- An overview of new trends on deep learning models for diabetes risk prediction 47
- A study on the detection and diagnosis of cervical cancer using machine and deep learning models 57
- Sentiments and opinions shared on social media during the COVID-19 pandemic using machine learning techniques 71
- Combining decision tree and Bayesian networks for improved predictive analytics 91
- Emerging trends in hybrid information systems modeling in artificial intelligence 115
- Hybrid approaches for improving cybersecurity and network intrusion system 153
- IoT security enhancement through blockchain solutions 167
- Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions 177
- Hybrid information systems for modeling traffic management and control 201
- Integrative hybrid information systems for enhanced traffic maintenance and control in Bangalore: a synchronized approach 223
- A comprehensive study for weapon detection technologies for surveillance under different YoloV8 models on primary data 241
- Strategic design of asymmetric graphene and ReS2 field-effect transistors using nonlinear optimization and machine learning 269
- Recent advancements in perfect difference networks for image recognition: a survey and analysis 307
- Image to text to speech: a web-based application using optical character recognition and speech synthesis 329
- Biomimicry and nature-inspired solutions for environmental sustainability 343
- Intelligent analysis of flowers and knowledge generation: an empirical study for agriculture 4.0 355
- Harnessing the power of hybrid models for supply chain management and optimization 407
- Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide 427
- Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide 443
- Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide 459
- Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide 473
- Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies 491
- Index 501
