Billet Punch Mark Identification Using Image Processing and Machine Learning

Sammanfattning

Traceability is important in any production facility. Alleima, a producer of stainless steel and alloys will also benefit from this by keeping track of its products. In this thesis, a billet punch mark system was developed to identify billets that arrive at an in-transit location and accurately detects and predicts the billet punch mark numbers. The system also includes a simple GUI (Graphical User Interface) which can be used at the facility. The punch marks will be saved along with the corresponding time of prediction, helping Alleima trace and correct any issues that might arise during production. Many steps were developed as part of this study to identify the punch marks: 1) template matching was used to find and crop the billet and billet holder, 2) SAD (Sum of Absolute Differences) was used to find a frame for each billet, 3) Canny filtering along with the Hough transform was applied to crop out the billet, 4) a YOLO (You Only Look Once) object detection model was trained to locate the punch mark region of interest (ROI), 5) the ROI was rotated using two rotation schemes, and 6) the numbers were read by OCR (Optical Character Recognition) software PaddleOCR. For the final evaluation, seven videos were processed. Out of a total of 292 billets, all were found, and the punch mark numbers (or lack thereof) were accurately detected in 92.5% of the cases.