Development of a Synthetic Data-Driven Pest Monitoring and Recognition System for Industrial Environments

Manual pest inspections in large warehouse environments are time-consuming, labor-intensive, and prone to human error, leading to potential contamination risks and inefficient monitoring processes. The client requires a modernized, automated system capable of accurately identifying pests under various environmental conditions without the need for extensive real-world data collection.

A mid-sized manufacturing company operating large warehouses seeking to automate pest detection and monitoring to enhance facility hygiene and prevent contamination.

• Create a digital environment model to simulate warehouse conditions for pest detection.
• Develop a camera simulation system that accurately reflects real-world camera settings and positions.
• Generate extensive synthetic datasets to train machine learning models for insect recognition and tracking.
• Implement a scalable system capable of functioning effectively across varying lighting and environmental scenarios.
• Reduce manual inspection workload and improve pest detection accuracy in monitored environments.

• Digital reconstruction of the warehouse environment including model creation of physical infrastructure.
• Simulated camera modules configured to match physical camera specifications and positions.
• Environmental and lighting scenario simulation supporting multiple conditions.
• Synthetic data rendering to produce labeled datasets for model training.
• Automated insect detection, classification, and activity tracking functionalities.
• Reporting tools for summarizing pest activity and generating actionable alerts.

• Camera hardware and settings configuration modules
• Data storage and management systems for synthetic datasets
• Reporting and alerting platforms

• System should support scalable data generation for extensive synthetic datasets.
• Achieve high accuracy in pest recognition with a target false positive rate below 5%.
• Ensure data security and access control for synthetic datasets and system reports.
• Design for real-time detection and reporting capabilities to facilitate prompt responses.

By leveraging synthetic data for training, the project aims to significantly reduce costs and time associated with real-world data collection, enabling faster deployment and deployment of a reliable pest detection system. The new solution is expected to decrease manual inspection efforts, improve detection accuracy, and support timely responses to pest activity, ultimately enhancing facility hygiene and operational efficiency.