OBJECT DETECTION AND IDENTIFICATION 1

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

Security and Surveillance

Enhanced object detection can significantly improve dock security, monitoring for unauthorised access, detecting suspicious activities, and identifying potential threats in real-time.

Maintenance and Inspection

Regular inspections of docks, equipment, and vessels using AI can preemptively identify areas requiring maintenance or repair, thus avoiding potential operational disruptions.

Dock Traffic Management

AI can assist in managing the movement of ships and vehicles within the dock area, optimising traffic flow, reducing congestion, and increasing overall efficiency while identifying various vehicles to adhere to security protocols.

Environmental Monitoring

AI can monitor environmental conditions at the docks, detecting any hazardous spills or emissions and ensuring compliance with environmental regulations.

Cargo an Supply Management

AI-driven systems can oversee cargo handling, ensuring accurate tracking, efficient loading and unloading, and minimising the risk of loss or damage.

Emergency Response Planning

In emergencies, AI can assist in the quick mobilization of resources, effectively managing evacuation procedures or other emergency responses.

Challenges

Inefficiency in Impurity Detection and Grading

The inefficiency in impurity detection and grading refers to challenges or shortcomings in the processes of identifying and classifying impurities within a system or production line.

Time-Intensive Evaluation Process

A time-intensive evaluation process refers to a method or system of assessment that demands a substantial investment of time. This extended duration for evaluation can pose challenges such as delays, increased resource utilization, and potential bottlenecks in decision-making.

Extensive Manpower Requirement

This could refer to a situation where a significant number of personnel is needed to fulfill specific tasks or roles within an organization, project, or industry.

Susceptibility to Human Error

This acknowledgment highlights the understanding that, in certain situations, the involvement of humans introduces the risk of errors due to factors such as fatigue, distraction, or lack of attention.

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.