You will get Machine-Learning (Computer-Vision) based trafic control system using Yolov8
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Top Rated
Project details
have successfully developed a system utilizing machine learning, specifically PyTorch with the YOLOv8 pre-trained model's nano version. This system is tailored for detecting cars, particularly light vehicles. The model underwent testing using a surveillance camera-recorded video feed on a two-way road with separate lanes for entering and exiting traffic.
The primary focus of the model is to exclusively detect cars, even amidst the presence of various vehicle types. It effectively counts both entering and exiting cars in real-time, providing instantaneous readouts of the net differences between the two. The underlying dataset for the model is COCO, enabling versatility in detecting and counting any specified vehicle type by name.
This system is well-suited for integration into diverse traffic management setups, offering a robust solution for vehicle detection and counting in real-world scenarios.
The primary focus of the model is to exclusively detect cars, even amidst the presence of various vehicle types. It effectively counts both entering and exiting cars in real-time, providing instantaneous readouts of the net differences between the two. The underlying dataset for the model is COCO, enabling versatility in detecting and counting any specified vehicle type by name.
This system is well-suited for integration into diverse traffic management setups, offering a robust solution for vehicle detection and counting in real-world scenarios.
Machine Learning Tools
OpenCV, Python, PyTorchWhat's included
| Service Tiers |
Starter
$100
|
Standard
$150
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 2 | 3 | Unlimited |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 0 | 1 | 2 |
Number of Graphs/Charts | 0 | 1 | 1 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$150 - $300
Additional Revision
+$50
Detailed Code Comments
(+ 2 Days)
+$100
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Hudson R.
Jul 25, 2025
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MC
Marcus C.
Jun 1, 2024
Data Analyst and Engineer
Amanuel is a very knowledgeable and efficient data engineer. A great asset to any team.
We will get back to in the future for additional projects.
We will get back to in the future for additional projects.
MN
Marco N.
Feb 5, 2024
Research and Compare various AI Models
I have known Amanuel for a long time now, he is my go-to person for all things AI. He is extremely knowledgeable and an expert in the field. I highly recommend him to everyone.
MN
Marco N.
Dec 26, 2023
Create an object detection AI model for refrigerators and turn it into an API
Amanuel is very good at his job. This is probably the 5th project we have worked together on for AI. He is clearly an expert in the field and has knowledge of various domains. Amanuel also completes jobs on time and of very high quality. Not only that, his prices are very affordable for people like me. This and much more is why I keep coming back to him. Give him a chance and I promise you that he won't disappoint. Thank you so much for the great experience once again Amanuel and hope to see you soon.
SP
Sailaja P.
Dec 12, 2023
Python Developer - Depth-Enhanced CNN Model Specialist
Couldn't complete entire work but part he done was good.
About Amanuel
Machine Learning | Computer Vision Expert
100%
Job Success
Gondar, Ethiopia - 8:41 pm local time
**** SERVICE AREAS ****
🟠 Machine Learning:
✅ Machine learning model development and implementation
✅ Data preprocessing and feature engineering
✅ Supervised learning (classification, regression)
✅ Unsupervised learning (clustering, dimensionality reduction)
✅ Deep learning and neural networks
✅ Natural language processing (NLP) tasks
✅ Recommendation systems
✅ Time series analysis and forecasting
✅ Model evaluation and performance optimization
🔴 Computer Vision:
✅ Image classification and object recognition
✅ Object detection and localization
✅ Image segmentation
✅ Facial recognition and biometrics
✅ Image and video analysis
✅ Feature extraction and representation learning
✅ Optical character recognition (OCR)
✅ Pose estimation and tracking
🟣 Model Evaluation and Performance Optimization:
✅ Model validation and evaluation metrics
✅ Hyperparameter tuning and optimization
✅ Performance optimization for scalability and efficiency
✅ Debugging and troubleshooting ML models
**** Proficient in the following PYTHON Libraries for ML/DL projects ****
✔️Tensorflow
✔️ Keras
✔️ PyTorch
✔️ Pandas
✔️ NumPy
✔️ SciPy
✔️ Python Scikit-Learn
✔️ Statsmodels
✔️ Matplotlib
✔️ OpenCV
✔️ PIL
**** Successfully Completed jobs in Upwork ****
➡ Emotion detection using CNN on the FER2013 dataset: Video Classification Deep Learning
➡ Stock price prediction using different predictive algorithms: Need an AI expert for stocks
➡ Computer vision using YOLOv5 PyTorch model: Weapon detection using Yolo and SURF Algorithm
➡ Building Predictive model for stocks (ForEex ): Build me a deep learning AI model from my data
➡ Build speech synthesis model using BARK, Tortoise, and SVC pre-trained models: Voice Cloning text to speech (custom voice)
➡ Create an object detection AI model for refrigerators and turn it into an API
➡ Python Developer - Depth-Enhanced CNN Model Specialist
➡ Research and Compare various AI Models
➡ Data Analyst and Engineer
**** Successfully Completed projects ****
✳ ML-based Stroke prediction
✳ Apple Price Prediction using RNN/LSTM
✳ Sign Language Recognition System using Random-Forest ML algorithm
✳ Alphabet Recognition (EMNIST) based on OpenCV, Convolutional Neural Network (CNN)
✳ Object Detection-DL and OpenCV
✳ ML-based Stroke prediction: LR, RNN/LSTM, Random-Forest, and Decision-Tree algorithms.
✳ AI/DL-based Autonomous navigating and patient follower robot using Nvidia Jetson Nano DK
Steps for completing your project
After purchasing the project, send requirements so Amanuel can start the project.
Delivery time starts when Amanuel receives requirements from you.
Amanuel works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset prepartion
I will use the dataset (if you need a custom-based model) and annotate the dataset to the specific object (vehicle) that needs to be detected. I will use LabelImg, LabelMe, VGG, or any other tools to annotate the image dataset.