AI Anomaly Detection

1. Market Need for AI Object and Anomaly Detection

1.1 Increasing Complexity and Scale:
As industries grow in complexity and scale, manual inspection and quality control processes become time-consuming, error-prone, and inefficient. AI object and anomaly detection solutions provide automation, enabling faster and more accurate identification of anomalies, improving quality control processes.
1.2 Quality Assurance and Compliance:
Maintaining product quality and adherence to compliance standards is crucial for industries such as manufacturing, healthcare, food production, and logistics. AI object and anomaly detection systems ensure that products meet quality requirements, detect anomalies, and reduce the risk of substandard products reaching the market.
1.3 Operational Efficiency:
By automating object and anomaly detection, organizations can streamline their operations, optimize resource allocation, and improve efficiency. Real-time detection and analysis of anomalies allow for prompt corrective actions, minimizing downtime and improving overall operational efficiency.

2. Main Users of AI Object and Anomaly Detection

2.1 Professional Auditors:
Professional auditors responsible for compliance inspections across various industries can greatly benefit from AI object and anomaly detection. These solutions provide an efficient and standardized way to detect and report anomalies during audits, reducing the time and effort required for manual inspections.
2.2 Quality Managers:
Quality managers in manufacturing and production facilities can leverage AI-powered detection systems to monitor product quality, identify defects, and ensure consistency in manufacturing processes. Object and anomaly detection solutions enable proactive quality control and facilitate continuous improvement initiatives.
2.3 Supply Chain and Logistics Professionals:
Professionals involved in supply chain and logistics operations can utilize object and anomaly detection solutions to detect irregularities, such as damaged packaging, incorrect quantities, or misplaced items. This enhances inventory management, reduces losses, and improves customer satisfaction.

3. Importance of AI Object and Anomaly Detection

3.1 Enhanced Accuracy and Efficiency:
AI-powered object and anomaly detection systems offer higher accuracy and efficiency compared to manual inspections. They can analyze vast amounts of visual data in real-time, enabling early detection of anomalies and reducing false positives or negatives.
3.2 Timely Error Detection and Resolution:
Automated detection allows for immediate identification and reporting of errors or anomalies. Organizations can take prompt corrective actions, minimizing the impact on operations and preventing further issues downstream.
3.3 Cost Reduction and Resource Optimization:
By automating object and anomaly detection, organizations reduce reliance on manual inspections, leading to cost savings and optimized resource allocation. Detection systems enable more efficient utilization of the workforce, freeing up employees to focus on more strategic tasks.

4. Qoodo Audit - Current Implementation:

The Qoodo audit system enables users to create custom protocols for compliance-related inspections. The following functionalities are provided within the audit system:
4.1 Protocols:
Users, such as Admin, Leader, and Sitemanager, can create custom protocols by defining control points and action plans. The protocols can be assigned to users, and guests can be invited to participate. The protocols are saved and can be accessed for review. Key features of the Protocols section include:
- Protocol Creation: Users can create protocols by providing a name, purpose, control points, action plan, and notification frequency.
- Protocol Management: The created protocols are displayed in a list format, showing relevant details such as creation date, notification type, and information provided during protocol creation.
- Assignees and Progress Tracking: Users can assign protocols to specific individuals and track the progress of audits.
- Notifications and PDF Reports: Users can enable/disable notifications and generate PDF reports of audit results.
- Protocol Editing and Deletion: Protocols can be edited to update information, control points, action plans, and notification frequencies. Deletion of protocols is possible with a confirmation pop-up.
4.2 Statistics:
The Statistics section provides an overview of saved protocols and their results on a daily, weekly, and monthly basis. Additionally, a protocol column is displayed on the site details screen to summarize monthly protocol results for individual users.

5. Anomaly/Error/Object Detector:

The anomaly detection feature in Qoodo utilizes computer vision techniques to identify and classify anomalies in real time. By pointing the camera at objects or areas of interest, the system can detect anomalies based on predefined error categories. The main functionalities of the anomaly detection module include:
Live Image Analysis: The Live Image Analysis function in the advanced AI-based audit System is a crucial component that leverages advanced computer vision techniques. When users point the camera at objects or areas of interest during inspections, the system immediately processes the live images in real time. Through this analysis, the system identifies various objects and relevant elements within the camera's view.
The sophisticated computer vision algorithms enable the system to accurately detect and recognize objects. This capability ensures a swift and efficient analysis of the visual data, laying the foundation for subsequent anomaly detection.
Anomaly Detection: Anomaly Detection is a pivotal feature powered by AI that sets the Qoodo Audit System apart from traditional audit tools. Once the Live Image Analysis has identified and categorized the objects, the system proceeds to the crucial task of anomaly detection.
Based on its internal database of predefined error categories, the AI system compares the observed objects and elements to known patterns. If any discrepancies, irregularities, or deviations are found, the system promptly alerts the user about the possible presence of anomalies.
To facilitate a more interactive experience, the system presents the user with a list of possible error options related to the detected objects. This empowers the user to select the most relevant anomaly category that accurately describes the observed irregularity. By involving the user in the process, the system refines its understanding of anomalies and improves its accuracy over time.
Customization: The Qoodo Audit acknowledges the diverse and unique requirements of different users and industries. To address this, the system offers a high degree of customization in anomaly detection. Clients have the flexibility to define and customize anomaly categories to suit their specific needs.
Users can predefine a set of anomaly categories that align with their particular inspection scenarios and quality standards. Additionally, the system will be able to train the itself to recognize new and previously unknown anomalies, continuously adapting to evolving audit requirements.
This customization feature ensures that the AI-powered anomaly detection aligns precisely with the user's inspection objectives, making the system highly adaptable and versatile for a wide range of applications.
Protocol Generation: The Protocol Generation function in the system streamlines the reporting and documentation process after anomaly detection. Once users have selected the relevant anomalies associated with the detected errors, the system saves this critical information for further processing.
The selected anomalies serve as the foundation for generating comprehensive protocols that detail the findings of the audit. These protocols can be easily created based on the detected errors, eliminating the need for manual documentation and minimizing human error.
The generated protocols are stored securely within the system and can be accessed and reviewed at any time. This valuable feature facilitates smooth communication, enables informed decision-making, and supports continuous improvement efforts in response to audit findings.