Original papersCalibrating cameras in an industrial produce inspection system

- A multi-camera calibration method for a real-time produce grading system is described.
- The method is totally unsupervised.
- The calibration target is a spheroidal ball with dot markers.
- Residual fitting (reprojection) error of 0.35 px is reliably achieved.
- Validation objects are reconstructed with 0.2 mm error.

We describe a multi-camera calibration method for a produce inspection system with color and monochrome cameras. The method uses a novel spheroidal calibration target that is similar in size to the produce being graded, and features a pattern of large and small dots. This enables us to calibrate the camera system for the localized volume through which the produce moves, where human access is impractical. We describe the detection and localization of the dot centres, and the process for putting dot images into correspondence with 3D points on the target. The calibration parameters are estimated via standard bundle adjustment techniques. The method reliably gives a reprojection error RMS of approximately 0.35 px, and is fully automated. We further validate the method by measuring error in sparse reconstructions of chessboard targets and the spheroid. These objects are reconstructed with approximately 0.2 mm RMS error. Finally, we use the calibrations to build 3D models of fruit and vegetables, achieving volume estimates within 7.3 mL (2.6%) of the true volumes.