Vehicle Detection and Classification using Image Processing
Vehicle Classification and Segmentation using Machine Learning, In this project we have used two dataset user have to upload image and it will generate segmented image and then it will predict the image like motorbike, car, etc. Machine Learning model is trained YOLOV8 Algorithm. Dataset contains number of classes like Car, Bus, Motorbike, Truck Van, etc. It will create the boundary box surrounding to vehicle in input image.
Vehicle detection is a vital capability for enabling intelligent transportation systems, autonomous vehicles, advanced driver assistance systems, and other automotive applications. By reliably detecting and localizing vehicles in real-time using cameras and sensors, vehicle detection enables traffic monitoring, pattern analysis, congestion mapping, toll collection, parking management, and driver assistance features like collision warning and lane departure alerts. However, robust vehicle detection is an extremely challenging computer vision problem, especially in complex urban environments.
Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy
Emergency Vehicle Detection using YOLO
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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