I have an image of a one-line substation schema diagram that includes various components (like transformers, circuit breakers, etc.) and the connections between them. I’m looking for a way to convert this image into a structured format (XML or JSON) that accurately captures the following details:
- Component Details: The exact coordinates and types of each element detected in the image.
- Connections: All the interconnections (lines) between the components.
- Reconstruction: The resulting XML/JSON should be sufficient to later reconstruct the original diagram.
So far, I’ve experimented with object detection models such as YOLO, but the results aren’t detailed enough, especially regarding the connections between components.
My questions are:
- Image Processing Techniques: What methods can be used to accurately detect both the individual components and the connecting lines? Are there any specific computer vision techniques or libraries (e.g., edge detection, Hough transform) that could help with this task?
- Deep Learning Approaches: Which deep learning models (like Mask R-CNN, DETR, etc.) or hybrid approaches could be effective for this problem, especially when pre-trained models don’t yield the necessary precision?
