A ruler recognition method based on rotated object detection
Strand tensioning is the core process in hybrid tower construction for wind turbines, and accurate detection of ruler scale markings is critical to construction quality. Traditional manual inspection and existing visual recognition techniques suffer from low efficiency, high cost, and excessive computational overhead. To address these challenges, we propose a novel ruler recognition method based on YOLOv11-OBB rotated object detection. The approach adopts the original detection algorithm without any model architecture modifications, and constructs a multi-scene dataset of ruler numerals and spray-painted regions using oriented bounding box annotations. After model training and inference with YOLOv11-OBB, a six-step post-processing pipeline is designed: (1) fitting the ruler straight line via the least squares method, (2) precisely locating the spray-painted box and its positioning point, (3) computing the minor scale value through coordinate calculations, (4) filtering effective major scale values, and (5-6) determining the final scale value based on the relative positional logic between the spray-painted box and major scale boxes. This method achieves end-to-end recognition with a single model, replacing the conventional multi-model pipeline, significantly reducing data annotation effort and computational resource consumption while ensuring accuracy and real-time performance. It can be flexibly deployed on computing devices with varying capabilities at construction sites.