We developed a web-based PWA for the visual inspection of cocoa beans. The solution combines image classification, object detection, and semantic segmentation with a practical infrastructure for data pipelines, experiment tracking, and edge devices.
Client/Company/Industry
QVISIONS GmbH
Duration
36 months
Product
Service
Expertise
Software Development
The goal of the project was to develop a web-based PWA for the visual inspection of cocoa beans. Image data was to be evaluated automatically, and the solution was designed to be used reliably both in model development and in a production-oriented environment.
A key challenge was combining different computer vision methods such as object detection, segmentation, and classification within one unified application. In addition, the data basis was difficult because assessments of the cocoa beans were sometimes contradictory. As a result, data collection, model training, and evaluation had to be aligned very carefully to achieve reliable results.
Programming Languages
Python
Technologies
DVC, Detectron, Docker, Docker-Compose, Flask, GitLab CI/CD, Keras, LabelStudio, MLFlow, NVIDIA Jetson, OpenCV, OpenVINO, PostgreSQL, PWA, TensorFlow, TensorRT, YOLO
Web-based application for the visual inspection of cocoa beans with camera control, image analysis, and AI-supported evaluation.
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The result was a web-based solution for the visual inspection of cocoa beans with integrated computer vision and machine learning components. Versioned data pipelines, experiment tracking, and the integration of edge devices created a solid foundation for practical use.
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