PLPR utilizes YOLOv5 and custom models for high-accuracy Persian license plate recognition, featuring real-time processing and an intuitive interface in an open-source framework.

This YOLOv5🚀😊 GUI road sign system uses MySQL💽, PyQt5🎨, PyTorch, CSS🌈. It has modules for login🔑, YOLOv5 setup📋, sign recognition🔍, database💾, and image processing🖼️. It supports diverse inputs, model switching, and enhancements like mosaic and mixup📈.

Simple process for camera installation, software and hardware setup, and object detection using Yolov5 and openCV on NVIDIA Jetson Nano.

Multi-backbone, Prune, Quantization, KD

This is a GUI interface which can process forest fire detection, smoke detection and fire segmentation. Yolov5 is used to detect fire and smoke and unet is used to segment fire.

This project uses YOLOv5, a state-of-the-art object detection model, to detect smoking in real-time video streams. The model is trained on a custom dataset, which is annotated to identify instances of smoking. The project also utilizes OpenCV, an open-source computer vision library, to process video frames and display the detected smoking instances

Using YOLOv5 and Image Processing for Detect Traffic State.

ESP32 CAM Cloud and Edge Computing for ethics monitoring in canteen using YoloV5 and Multithreading Processes with Supervisor

In developing nations such as India, the vehicular growth rate is increasing exponentially which is worsening the traffic operations. Most of the urban cities in India are facing traffic related problems such as congestion, accidents, pollution, etc. during peak hours. The main cause for traffic congestion in such cities is mainly due to uncontrolled urbanization and extensive usage of private vehicles. The traffic congestion leads to many problems like increase in travelling time, health disorders and accidents. Road accidents in India claimed over 1.5 lakh lives in the country in the year 2018, with over-speeding of vehicles being the major cause. The Ministry of Road Transport and Highways report on Road accidents in India stated that road accidents increased by a rate of 0.46 % in the year 2018 when compared to 2017 . Due to this there is a need to develop a model which can analyze and detect poor road conditions like potholes.This project aims in building a system which can detect the poor road conditions and can notify the driver as well as the government beforehand to improvise the road conditions. Pothole detection is being carried out using two techniques namely image processing and machine learning techniques. Those two techniques are used for a study of the detection and occurrence of potholes. In this project, we implemented both of them individually and then a combination of the techniques to see how image pre-processing can affect the performance of a deep learning model. The image pre-processing steps like erosion,median blur etc applied in this project removes the noise in the image which helps in better training of the model.First of all, we implemented the image processing techniques on a single image in the order: median blur, erosion, canny edge detection, contour detection, bounding box prediction. After that we labelled a dataset of around 800 images and passed it to the YOLOv5 model and noted the results.Secondly, we applied median blur on the already labelled dataset and then passed it to the YOLOv5 model and noted the results. Lastly, we applied median blur and erosion both and passed it to the model and noted the results. We compared the results at last. The conclusions reached are that a combination of machine learning and image processing techniques generates good performance in pothole detection and machine learning techniques provide better results than the usual image processing models.

YOLOv5 pre-processing and post-processing（GPU and CPU）

An automated pipeline that performs daily object detection on Columbus Circle EarthCam images using YOLOv5, deployed with AWS Lambda for seamless cloud processing and visualization.

People Detector is a Python script that processes videos as input and performs individual people detection, tracking, and counting, using YOLOv5 and motpy. It then displays bounding boxes around each person, assigns unique IDs, and shows the count of people in the video frame.

IMAGE PROCESSING > UNET > YOLOv5 > CNN > CONTROLLER

Autonomous chase rover on Jetson Nano - RealSense D435 depth camera, YOLOv5 detection, tank tracks, 5-process Python pipeline

Detect football players in videos using YOLOv5 for training and YOLOv8 for inference. The dataset is sourced from Roboflow and includes 663 annotated images. The project involves pre-processing, augmentation, and model training for accurate player detection.

The Sign Language Detection System is an end-to-end deep learning application that recognizes sign language hand gestures using the YOLOv5 real-time object detection model. It processes images or live camera feeds and runs through a Flask web server, with automated deployment to AWS using Docker and a GitHub Actions–based CI/CD pipeline.

[YOLOv5] [TensorFlow.js] DESIGNING A PLANT IDENTIFIER THROUGH LEAF IMAGE PROCESSING

OCR system for vehicle license plate detection and recognition using PyTorch, YOLOv5, and ONNX, with automated data processing and results exported to CSV.

YOLOv5 + DJI Tello Drone 🚁 Real-time object detection using a DJI Tello drone and YOLOv5. Stream live video via djitellopy, process frames with PyTorch + OpenCV, and detect objects on the fly. Great for AI + drone automation projects.

Real-time Person Tracking with YOLOv5 and SORT: This project uses YOLOv5 for object detection and the SORT tracker for real-time tracking in videos. It processes video frames to detect and track persons, drawing bounding boxes and IDs on the output. Supports video file input and outputs the processed video.

Real-time drowsiness detection system using YOLOv5 to monitor driver alertness through eye closure and yawning detection. Processes video at 137 FPS with 99.2% mAP@0.5.

An AI-powered autonomous drone that detects humans using a YOLOv5 model and delivers payloads with precision, ideal for disaster relief. It uses Jetson Xavier for onboard processing and supports real-time mission control via a web dashboard.

Aalto University project for course CS-E4875. Simulates traffic with CARLA and processes sensor data in a DES simulation with YOLOv5 object detection to create a 2D world for congestion analysis.

YOLO Vision and Real-Time Detection utilizes YOLOv5 to perform real-time object detection on video streams. This project processes live videos to identify and label objects with bounding boxes and confidence scores. It includes input and output video demonstrations, showcasing YOLOv5’s ability to detect objects such as people, phones, and remotes

An underwater remotely operated vehicle (ROV) system designed for crack detection, corrosion assessment, and real-time surveillance. The system integrates sensor data with a Raspberry Pi and uses machine learning (YOLOv5) for underwater image processing. The project aims to enhance underwater inspections and safety.

This notebook includes all the steps of the process of building an Object Detection Pipeline: Training, validation, test, based on YOLOv8 (and also a YOLOv5 model). The dataset used has been labeled and treated previously in Roboflow.

Tennis Tracking and Region Detection -A YOLOv5-based project for real-time tennis ball and player tracking. Built with Python and Streamlit for an interactive interface, it processes videos to identify and analyze player movements and ball trajectories. Perfect for sports analytics and AI-powered performance

This is a Kotlin-based Android app for real-time object detection using a YOLOv5 model converted to TensorFlow Lite (`.tflite`). It uses CameraX to capture live video, processes frames with the TFLite model, and displays detected objects with labels and confidence scores. The app runs fully offline and is optimized for mobile devices.

First, a variety of YOLO models are selected for training. During the training process, parameters are constantly modified according to various indicators of model evaluation to adjust the model structure. Finally, the YOLOv5 model with high accuracy and the YOLOv3 tiny model with high frame rate are selected. Then, the front and rear ends are built, and functions such as data statistics, image detection based on YOLO model, and real-time monitoring of helmet wearing by video stream are realized.

IMAGE PROCESSING > UNET > YOLOv5 > CNN > CONTROLLER