- **Project Type: Object detection
- **Operating System & Browser: Microsoft Edge
I am currently working on a project in the field of electrical engineering that involves automating the process of extracting material take-off (MTO) data from single-line and multi-line diagrams (SLD/MLD). For this, I’ve developed a custom annotation strategy using Roboflow, aiming to train a model that can accurately detect and classify engineering components and symbols.
To facilitate this, I’ve created three annotation classes:
Component_Block: Used for bounding both symbols and associated text.
Text_Block: Used for bounding text-only descriptions that specify component characteristics but do not require associated symbols.
Symbol_Block: Used for bounding symbols-only, with no associated text.
Using these labels, I trained a YOLO model and achieved an initial mAP@50 of about 0.3 with a quantity of 60 images. While this is a promising start, I’m looking to significantly improve performance and reduce errors.
I would greatly appreciate any guidance from the community regarding:
I want to know how this method works in this type of task,
Best practices in fine-tuning for symbol-text annotation tasks,
Augmentation strategies or preprocessing tips that worked well for similar domains,
Any tricks to improve model performance on technical diagrams or documents.