cast defect Computer Vision Project

Project Title: Automated Defect Detection in Casted Metal Parts

Project Overview: This project aims to develop a computer vision-based system capable of autonomously inspecting and identifying defects or imperfections in casted metal parts. The system utilizes state-of-the-art deep learning techniques to automate the quality control process, ensuring that only high-quality parts proceed to further manufacturing stages. By leveraging the power of artificial intelligence (AI) and machine learning (ML), the system enhances accuracy, consistency, and efficiency in defect detection.

Description of Each Class Type: The project involves classifying casted metal parts into two categories:

Defective Parts: This class includes casted metal parts that exhibit one or more defects or imperfections. Defects can range from surface irregularities, cracks, or incomplete casting.

Non-Defective Parts: This class comprises casted metal parts that meet the required quality standards and exhibit no detectable defects.

Current Status and Timeline:

Project Initiation: The project began in [Insert Start Date] with the goal of automating defect detection in the manufacturing process. Data Collection: Extensive data collection of images of casted metal parts, both defective and non-defective, was completed by [Insert Data Collection Period]. Model Development: The development of the defect detection model, using the MobileNet V2 architecture, commenced in [Insert Model Development Start Date]. Model Training: The model was trained over [Insert Training Duration] epochs to achieve high accuracy and consistency in defect detection. Validation: The model's performance was validated on a separate dataset to ensure its reliability in real-world scenarios. Ongoing Refinement: The project is currently in the refinement and optimization phase, where fine-tuning and improvements are being made to enhance the system's capabilities. Deployment: The deployment of the automated defect detection system in manufacturing facilities is scheduled for [Insert Deployment Date]. Links to External Resources:

Link to MobileNet V2 Documentation ImageNet Dataset Contribution and Labeling Guidelines:

Data Labeling: Data labeling involves categorizing each image as either "Defective" or "Non-Defective." Annotators should follow a standardized labeling protocol to maintain consistency. Model Development: Contributions to the model development involve refining the MobileNet V2-based architecture, fine-tuning hyperparameters, and optimizing the system for speed and accuracy. Deployment: Collaborators are responsible for deploying the trained model in manufacturing facilities, ensuring seamless integration with existing quality control processes. Continuous Improvement: The project encourages continuous improvement, with contributions focused on enhancing the system's adaptability to new defect types and production scenarios. By combining cutting-edge technology with industrial needs, this project strives to revolutionize quality control in manufacturing, ensuring that casted metal parts meet the highest standards of quality, reliability, and safety.