Overview / Usage

The project aims to develop an AI model using YOLOv5 for detecting scrap objects in images and estimating their sizes. This addresses the challenge of automating the process of scrap detection, which is crucial in various industries such as manufacturing, recycling, and waste management.

By automating scrap detection, the model can help improve efficiency, reduce manual labor, and enhance safety in industries dealing with scrap materials. Additionally, accurate size estimation of scrap objects provides valuable information for inventory management, logistics, and resource allocation.

In production environments, this AI model can be integrated into existing systems for real-time scrap detection on conveyor belts, sorting lines, or during quality control inspections. This can streamline operations, minimize waste, and optimize resource utilization, ultimately leading to cost savings and improved productivity.

Furthermore, the ability to deploy the model on edge devices or in the cloud enables scalability and adaptability to different production environments, making it a valuable tool for a wide range of industries seeking to automate and optimize their processes.

Methodology / Approach

Our methodology for solving the problem of scrap detection and size estimation involves several key steps:

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1. Data Collection and Preprocessing: We gather a diverse dataset of images containing scrap objects of varying sizes, shapes, and orientations. The images are annotated with bounding boxes indicating the location and size of each scrap object. We preprocess the images to enhance quality, remove noise, and standardize the format.

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1. Model Selection: We choose YOLOv5 as our object detection model due to its state-of-the-art performance, speed, and ease of use. YOLOv5 is based on the YOLO architecture and utilizes deep learning techniques to detect objects in images efficiently.

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1. Training: We train the YOLOv5 model on the annotated dataset using transfer learning. We fine-tune the pre-trained model weights on our specific dataset to adapt it to the task of scrap detection. During training, we optimize the model parameters to minimize the detection loss and improve accuracy.

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1. Deployment: Once the model achieves satisfactory performance, we deploy it in production environments. We integrate the model into existing systems or develop standalone applications for real-time scrap detection. We optimize the deployment process for efficiency, scalability, and compatibility with edge devices or cloud platforms.

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Frameworks and standards used in our development include:

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• PyTorch: A popular deep learning framework used for building and training neural networks.

• OpenCV: An open-source computer vision library used for image processing, manipulation, and analysis.

• YOLOv5: A deep learning model for object detection, implemented using PyTorch.

• Transfer Learning: A technique where a pre-trained model is fine-tuned on a specific task or dataset to improve performance.

• Data Augmentation: Techniques such as random cropping, rotation, and flipping are used to increase the diversity of the training dataset and improve the robustness of the model.

• Model Optimization: Techniques such as pruning, quantization, and model compression may be applied to optimize the size and speed of the deployed model, particularly for edge deployment scenarios.

Technologies Used

1. Intel CPUs: Central processing units manufactured by Intel Corporation for general-purpose computing.
2. Intel Integrated Graphics: Graphics processing units integrated into Intel CPUs for hardware acceleration.