Line Scan Camera Defect Detection

Line scan cameras create long, continuous images of moving materials such as steel, fabric, and coated films, but these strips are poorly suited to object-detection models that require smaller, roughly square inputs. The described Roboflow Workflow addresses this by using an Image Slicer to divide strips into overlapping 640×640 tiles, an RF-DETR model to detect defects in each tile, and Detections Stitch to remap predictions to the original image and use non-maximum suppression to remove duplicates at overlapping boundaries. Overlap preserves complete views of defects that might otherwise be split across tiles, while stitching consolidates repeated detections into single boxes. The tutorial uses a 1,900-image steel surface defect dataset with six classes, trains an RF-DETR model that achieved 74.7% mAP@50, and creates a synthetic strip by joining test images to demonstrate the approach. For production use, the workflow can be adapted with real line-scan imagery and models trained on specific materials, with tile dimensions, overlap, confidence thresholds, and duplicate suppression tuned to balance detection accuracy and processing speed.

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