Computer Vision Expert
Budget: $750 – $1,500 CAD
Introduction:
This project aims to develop and deploy machine learning models for object detection and segmentation tasks, with a focus on safety and security applications. The project will utilize the YOLO and Python programming language to implement these models.
The specific focus areas for object detection include hard hat detection, fire detection, personal protective equipment (PPE) detection (such as safety glasses and face mask detection), and intruder detection.
By leveraging state-of-the-art computer vision techniques, you will deliver production ready trained models.
These models will be designed to identify and classify specific objects of interest, including hard hats, safety glasses, face masks, fires/flashes, and potential intruders. The project will strive to achieve high precision and recall rates, ensuring reliable detection and minimizing false positives/negatives. Additionally, the models will be optimized for real-time performance, enabling rapid decision-making and response in safety-critical scenarios.
Expected Outcomes:
By the end of this project, we anticipate the following outcomes:
Highly accurate and efficient object detection and segmentation models for hard hats, safety glasses, face masks, fires/flashes, and potential intruders.
Deployable framework integrating the trained models, enabling real-time monitoring and detection in safety-critical environments.
This project aims to develop and deploy machine learning models for object detection and segmentation tasks, with a focus on safety and security applications. The project will utilize the YOLO and Python programming language to implement these models.
The specific focus areas for object detection include hard hat detection, fire detection, personal protective equipment (PPE) detection (such as safety glasses and face mask detection), and intruder detection.
By leveraging state-of-the-art computer vision techniques, you will deliver production ready trained models.
These models will be designed to identify and classify specific objects of interest, including hard hats, safety glasses, face masks, fires/flashes, and potential intruders. The project will strive to achieve high precision and recall rates, ensuring reliable detection and minimizing false positives/negatives. Additionally, the models will be optimized for real-time performance, enabling rapid decision-making and response in safety-critical scenarios.
Expected Outcomes:
By the end of this project, we anticipate the following outcomes:
Highly accurate and efficient object detection and segmentation models for hard hats, safety glasses, face masks, fires/flashes, and potential intruders.
Deployable framework integrating the trained models, enabling real-time monitoring and detection in safety-critical environments.