Thanks for you answer.
The task is to pick an bent ironbar (with 1 or more bents) on a vertical plane, where the background is not well defined ( gears and devices, shadows, reliefs)
I use a Sapera GigE Genie Nano.
I've done all the software to detect the piece and transfer the coordinate to the robot using a TCP/IP Protocol and it works fine.
For first, I tested on a white table and to convert the coordinates from pixel to mm, I just use the known measurements of the piece, frame with a fixed camera (pixelsPerMillimeters = f.size().width/widthPiece). All was good, and the robot moves on the right coordinate with the right orientation of the end-effector.
The main areas to consider are usually:
- Ambient light control to avoid exposure issues and incorrect detection.
- Accurately 'trained' detection methods.
- Speed of detection, to transfer of data to decoding of data.
- Accurate defined relative/FRAME of robot to camera field.
Now, i want to generalize all and test on a vertical planar surface.
I've done a more efficient algorithm for detection using RCNN algorithm (just 100 images in train, so it could be powered for sure) .
- Detection works but is slow, any hints to improve?
- Studying on books and reading some algorithm on the web, i found the calibration method with the use of a printed chessboard.
Now, I want understand more of this and how it works.
- I applied a IR flashlight (smart vision light l300) behind the camera but for sure it could be improved with different position and settings
Hints?
Thank you