EGG COUNT | CONVEYOR - OBJECT DETECTION
Challenge
Businesses in the food and agriculture sector often struggle to accurately count eggs on conveyor belts.
- Manual counting is time-consuming, error-prone, and labor-intensive, causing delays and inconsistencies.
- Operational inefficiencies lead to wastage, mispackaging, and higher labor costs.
Approach
- Collect and preprocess video feed and image data from conveyor belts.
- Apply object detection models, such as YOLO or Faster R-CNN, to identify and count eggs in real time.
- Account for conveyor speed, overlapping eggs, and lighting variations during model training to ensure robust detection.
- Develop a monitoring dashboard to display counts and trigger alerts for discrepancies.
Data
- Image frames captured from conveyor belt cameras
- Egg positions, size, and orientation
- Conveyor belt speed and lighting conditions
- Annotations for training the object detection model
Solution
- Computer vision based object detection system that automatically counts eggs on conveyor belts.
- Dashboard reporting that provides operational insights and tracks historical trends.
- Scalable system capable of handling multiple conveyor belts simultaneously, ensuring consistent performance.
Business Impact
- Automated egg counting, significantly reducing manual labor and human error.
- Enhanced operational efficiency, enabling faster throughput and minimizing wastage.
- Improved quality control by detecting missing eggs in real time.
- Cost savings through reduced labor requirements and improved process reliability.