Development of an Automated Image Classification System for Metal Scrap Identification

The client faces difficulties in efficiently and accurately classifying scrap metal images, leading to potential delays, incorrect sorting, and increased operational costs. They require a solution that enables real-time image-based classification to streamline their recycling process and support environmental sustainability goals.

A manufacturing company specializing in recycling steel and nonferrous metals, aiming to optimize scrap metal sorting and classification processes.

• Develop and deploy a machine learning-based image classification model trained on scrap metal photo datasets.
• Integrate the classification model into a mobile application for real-time image capturing and instant classification by operators.
• Create an administrative web interface for managing classifications, monitoring system performance, and overseeing scrap yard operations.
• Host the machine learning model in a cloud environment to ensure scalability, robustness, and remote accessibility.
• Improve classification accuracy and processing speed to support operational efficiency and environmental sustainability initiatives.

• A trained machine learning model capable of classifying scrap metal images with high accuracy.
• Mobile application allowing users to take photos of scrap metal and receive instant classification results.
• Web application for managing classification data, viewing historical results, and overseeing scrap yard operations.
• Secure cloud hosting of the ML model ensuring high availability and scalability.
• Real-time classification capability with swift response times suitable for operational environments.

• Cloud computing services for hosting the ML model
• Mobile device camera hardware for image capture
• Database systems for classification and operational data management
• Security protocols for user authentication and data protection

• High model accuracy with at least 90% precision in classification
• System response time under 2 seconds for real-time classification
• System availability of 99.9% uptime
• Data security compliance for sensitive operational data
• Scalable architecture to accommodate increased image data volume

The implementation of this automated classification system is expected to significantly increase sorting accuracy, reduce manual effort, and accelerate processing times. Projected outcomes include improved operational efficiency, a decrease in classification errors, and enhanced sustainability through optimized material recovery processes.