OBJECT DETECTION AND IDENTIFICATION

Advancements in AI, particularly through Convolutional Neural Networks (CNNs) and algorithms like YOLO (You Only Look Once), have revolutionized object detection. YOLO v5’s ability to process an entire image at once dramatically increases accuracy and speed, enabling real-time analysis. This integration has significantly enhanced object detection efficiency, proving vital across industries from manufacturing quality control to surveillance

Applications

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Challenges

Inefficiency in Impurity Detection and Grading

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Time-Intensive Evaluation Process

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Extensive Manpower Requirement

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Susceptibility to Human Error

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METHODOLOGY

Employing the concept of object detection, a convolutional neural network (CNN)-based YOLO algorithm was applied to accurately identify and locate impure instances like Breakage, Dot and Inclusion along with clean instances in an image.

KEY FINDINGS

REDUCTION IN MANUAL ERRORS

The automated system significantly minimized errors (close to 5% as compared to 20% before) that were previously common in manual processes. By leveraging advanced algorithms and precise machinery, the system ensured a higher level of accuracy in operations, leading to improved product quality and consistency.

TIME EFFICIENCY

The introduction of automation resulted in a notable decrease in the time required to complete tasks, from an average of 30-60 minutes to 1 minute maximum.

ENHANCED GRADING ACCURACY

In quality control, the automated system demonstrated superior grading accuracy. Its ability to meticulously assess and classify products based on pre-set criteria ensured a consistent evaluation.

REDUCED LABOR COSTS

The transition to an automated system reduced the need for a large manual workforce, leading to substantial cost savings in terms of labour.

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