Muhammad Yahya’s Post

Hello LinkedIn community, I am excited to share with you a project I recently worked on that combines the power of OpenCV and YOLOv8 to create a real-time car counter. In this project, I utilized OpenCV, a popular computer vision library, along with YOLOv8 , a state-of-the-art object detection model, to detect and count cars in real-time from a video feed or a live camera. The real-time car counter has a wide range of applications, including traffic flow analysis, parking lot management, toll booth monitoring, and smart city initiatives. Ultralytics #OpenCV #YOLOv8 #ComputerVision #DeepLearning #TrafficManagement #AI #MachineLearning #ObjectDetection #PythonProgramming #RealTime

🚗🔍 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭: 𝐂𝐚𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐔𝐥𝐭𝐫𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐘𝐎𝐋𝐎 𝐯𝟖 𝐚𝐧𝐝 𝐎𝐩𝐞𝐧𝐂𝐕 🔍🚗 I'm excited to share a project I've been working on that leverages the power of Ultralytics YOLO v8 and OpenCV for car detection and counting. 🎉 In this project, I developed a system capable of accurately detecting cars and counting them within manually selected lanes. This approach ensures precise monitoring and data collection for traffic management and analysis. Key Features: ✅ 𝐇𝐢𝐠𝐡-𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐂𝐚𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Utilizing YOLO v8's advanced capabilities for real-time object detection. ✅ 𝐂𝐮𝐬𝐭𝐨𝐦 𝐋𝐚𝐧𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Manual lane selection for targeted car counting, enhancing flexibility and accuracy. ✅ 𝐎𝐩𝐞𝐧𝐂𝐕 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Efficient processing and visualization of detection results. This project showcases the potential of combining cutting-edge machine learning models with robust computer vision tools to solve real-world problems. I'm looking forward to exploring further applications and enhancements in this domain. Thanks to InLights and Muhammad Kawish Sharif for this awesome learning experience. Feel free to reach out if you're interested in learning more or discussing potential collaborations! #MachineLearning #ComputerVision #YOLOv8 #OpenCV #TrafficManagement #AI #DeepLearning #ProjectShowcase

I want to share a project using the latest version of YOLO object and segmentation model which is Yolov8 developed by Ultralytics 🚀. There were only 200 images to train but with Data Augmentation tools such as crop, rotation, blur, etc. it can considerably increase the number of images and have better accuracy. This model was trained in Google Colab using its GPU 💻 and can detect when a person is using a safety helmet 👷♂️ or not 🙎♂️. This can help industries have better control over the use of EPP by workers and contractors and send an alarm when the safety helmet is not being used. The results were good running in an imported video and in real time on my webcam. Even it is possible using OpenCV functions to modify webcam frames and create a new frame in which we are only interested in detecting in a specific area as seen in the video. #AI #ComputerVision👁 #DeepLearning #Yolo #ObjectDetection #Ultralytics #Roboflow #Security

🚨 Introducing Multi-Hazard Detection System 🚨 Excited to share my latest project, a deep learning-based computer vision system designed to detect multiple hazards in real-time. This project utilizes YOLOv11, powered by PyTorch and OpenCV, to monitor and identify critical risks in live video feeds. 💡 Key Features: - Smoking Detection: Identifies people smoking cigarettes to enforce no-smoking zones. - Fire Detection: Recognizes fire outbreaks for faster emergency response. - Vehicle Detection: Tracks vehicles for safety and security. - Crowd Density Monitoring: Measures crowd size to prevent potential risks. - Handgun Detection: Enhances security by detecting firearms in public and private spaces. Each feature is optimized for real-time performance, detecting multiple hazards simultaneously in complex environments. This system offers vast applications in public safety, security, and emergency response. Looking forward to future improvements and real-world deployments! GitHub link: https://lnkd.in/gTqnJXka #DeepLearning #ComputerVision #AI #YOLOv11 #PyTorch #OpenCV #HazardDetection #RealTimeAI #SafetyTech

🚨 Exciting Project Alert: Real-Time Wrong-Way Driving Detection with YOLOv8, OpenCV, and Telegram Integration! 🚗🚦 I'm thrilled to share a project I've been working on that combines the power of computer vision with real-time monitoring to enhance road safety. 🚘 🔍 Project Overview: In this project, I've implemented a Wrong-Way Driving Detection System using YOLOv8 for object detection, OpenCV for real-time video processing, and cvzone for advanced visual overlays. The system detects cars driving in the wrong direction on a predefined road segment, triggers an alarm, and sends an alert to a Telegram bot with a snapshot of the incident. 📸🔔 💻 Technical Highlights: YOLOv8: Leveraged for detecting vehicles with high accuracy. OpenCV: Used for video stream processing and real-time frame analysis. cvzone: Enhanced the visualization of detected vehicles and their paths. Telegram Bot Integration: Sends real-time alerts, ensuring immediate response. 🔧 How It Works: The system defines two key areas on the road using polygonal regions. If a vehicle enters the exit area (area2) without passing through the entry area (area1) first, it's flagged as driving the wrong way. Upon detection, an alarm is triggered, the incident is logged, and a notification is sent via Telegram with a photo of the vehicle. This project showcases how AI and computer vision can be utilized to address real-world safety challenges on the road. 🌍 I'm excited about the potential of this system to contribute to safer roads and look forward to exploring more innovations in this space. 🚀 If you're interested in the details or have ideas to improve this project, feel free to reach out! Let’s collaborate and make the roads safer together. #YOLOv8 #OpenCV #cvzone #ultralytics #ArtificialIntelligence #ComputerVision #TechInnovation #SmartCities #WrongWayDriving #WrongWayDetection #TrafficViolationDetection #VehicleTracking #RoadMonitoring #TrafficSafety #DrivingSafety #WrongWayPrevention #SmartTrafficSolutions #AutomotiveSafety #WrongWayAlert #AccidentPrevention #TrafficSurveillance #SafetyTechnology #RoadHazardDetection #IntelligentTrafficSystems #VehicleDetection #RoadSafetyTech #HighwaySafety #TrafficViolationAlert

🚗Excited to share our team's recent project on vehicle detection and counting using OpenCV!🚗 I would like to show our gratitude towards Vipul Mishra sir for guiding us throughout the project. During my exploration of computer vision applications, I embarked on this project to develop a system that can detect and count vehicles in a given video feed. Leveraging the power of OpenCV, I implemented a robust algorithm that can accurately identify vehicles, track their movements, and provide real-time counting. Key highlights of the project: Utilized Haar cascades for vehicle detection to achieve high accuracy. Implemented background subtraction techniques to isolate moving vehicles. Used contour detection to track vehicles and maintain count. Achieved real-time performance, suitable for live traffic monitoring. This project not only enhanced my understanding of computer vision but also showcased the practical applications of OpenCV in traffic management and surveillance systems. I'm excited to continue exploring the possibilities of this technology in future projects! #ComputerVision #OpenCV #VehicleDetection #Traffic Management #Al #Machine Learning #DeepLearning#GSV

Project Title: Detect vehicle and check whether it is positioned in the center. "Vehicle Detection with YOLO (You Only Look Once) and OpenCV: This project utilizes the YOLO object detection algorithm, implemented using OpenCV, to detect and highlight vehicles in videos, images, and even URLs. The system identifies vehicles, checks if they are in the center of the frame, and displays the results with bounding boxes and labels. Whether analyzing video files, image files, or images from URLs, this project offers a versatile solution for real-time vehicle detection and positioning." #objectdetection #computervision #YOLOAlgorithm #opencv #VehicleRecognition #deeplearning #intelligenttransportation #imageprocessing #machinelearning #linkdin

🌟Demystifying Real-time Object Detection with Machine Learning using MobileNetSSD Are you curious 👀 about how computers can "see" and identify objects in images and videos? This is the power of object recognition in machine learning! Today, I'm sharing a glimpse into a simple object recognition program using the popular MobileNetSSD model. This program utilizes a pre-trained model to detect and classify various objects in real-time through a webcam feed. Here's a breakdown of the code: ✷ Loading the Model: We use OpenCV to load the pre-trained MobileNetSSD model files. ✷ Defining Object Classes: The program can recognize various objects like cars, people, animals, and furniture. ✷ Real-time Detection: The program captures video frames from the webcam and resizes them for processing. ✷ Object Bounding Boxes & Labels: Detected objects are highlighted with bounding boxes and classified labels with confidence scores. Applications of Object Recognition: Object recognition has a wide range of applications, including: 1. Self-driving cars 2. Robotics and automation 3. Image and video analysis 4. Surveillance systems 5. Content moderation GitHub link : http://surl.li/sojsn #MachineLearning #ObjectRecognition #ComputerVision #OpenCV #MobileNetSSD

🔥 Following previous posts about running privacy AI, some good progress on my "AI in a box". Featuring touch screen needs some changes for power management. Features: 📷 CV/Edge AI with OAK - Face recognition + pan-tilt 🔊 / 🎤 Quality speaker and microphone 👆 10" touch screen 🧠 Local LLM + TTS + Speech recognition (local but external host support) 🔋 Easy power management and connection Full demo coming soon 😉 #madewithunity #digitaltwins #ros2 #robotics #computervision #unity3d #simulation #ai #diy #depthai #spatialcomputing #edgeai #deeplearning #artificialintelligence #machinelearning #innovation #syntheticdata #perception #privacyfirst #llms

Excited to share our team's recent project on vehicle detection and counting using OpenCV! I would like to show our gratitude towards Vipul Mishra for guiding us throughout the project. During my exploration of computer vision applications, I embarked on this project to develop a system that can detect and count vehicles in a given video feed. Leveraging the power of OpenCV, I implemented a robust algorithm that can accurately identify vehicles, track their movements, and provide real-time counting. Key highlights of the project: Utilized Haar cascades for vehicle detection to achieve high accuracy. Implemented background subtraction techniques to isolate moving vehicles. Used contour detection to track vehicles and maintain count. Achieved real-time performance, suitable for live traffic monitoring. This project not only enhanced my understanding of computer vision but also showcased the practical applications of OpenCV in traffic management and surveillance systems. I'm excited to continue exploring the possibilities of this technology in future projects! #ComputerVision #OpenCV #VehicleDetection #TrafficManagement #Al #Machine Learning #DeepLearning#GSV