Incident Detection Website Using Computer Vision
1 Prof.Sunil Yadav, 2 Bhushan Patil , 3 Avadhut Patil, 4 Babasaheb Nagile, 5 Vishal Lokhare
1 Professor, Dr. D. Y. Patil College of Engineering and Innovation, Varale, Pune, India
2Student, Dr. D. Y. Patil College of Engineering and Innovation, Varale, Pune, India
3Student, Dr. D. Y. Patil College of Engineering and Innovation, Varale, Pune, India
4 Student,Dr. D. Y. Patil College of Engineering and Innovation, Varale, Pune, India
5 Student, Dr. D. Y. Patil College of Engineering and Innovation, Varale, Pune, India
Abstract : The Incident detection website using computer vision that utilizes machine learning algorithms and computer vision techniques to detect various incidents, including car accidents, violence, fire accidents, and weapons detection. The system aims to enhance safety, security, and emergency response capabilities by providing real-time alerts via SMS to relevant authorities upon incident detection. However, the design model enables the end user to instantly report the most frequent casualties that occur in public, such as fires, automobile accidents, violence, and weapons (guns), by simply visiting the website and uploading the causality image, which will immediately report to the relevant authority.
While the system aims to improve emergency response, it is important to note its limitations. Factors such as environmental conditions, image quality, and lighting variations may affect the system's accuracy and reliability. The system serves as an aid to enhance emergency response capabilities and should not replace the role of human responders. The incident detection system offers a valuable tool for enhancing public safety, facilitating rapid incident response, and minimizing the consequences of accidents or violent incidents. Its deployment can contribute to a proactive and efficient approach to emergency management.
Disasters and emergencies, whether natural or man-made, often result in chaotic and life-threatening situations where rapid response is crucial. The ability to swiftly and accurately identify casualties amidst the chaos can significantly improve the effectiveness of disaster response efforts.
This project proposes the development of an innovative Computer Vision system aimed at automating incident detection and victim identification, leveraging advancements in image processing and deep learning techniques.
IndexTerms - Disease detection, CNN (Convolutional Neural Network),
