ArticleScientific reports2026
Efficient adaptive rotated object detection for 1D and QR barcodes.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
This study introduces EA-OBB, a lightweight rotated object detection framework designed for detecting one-dimensional (1D) and Quick Response barcodes. Built upon the YOLO11 architecture, EA-OBB integrates several innovative modules-KWConv, ORPNCSPELAN, and LADH-OBB-to enhance both accuracy and computational efficiency in rotated object detection. The KWConv module utilizes a dynamic convolution kernel mechanism to improve rotational barcode feature extraction. The ORPNCSPELAN module enhances computational efficiency through multi-path feature aggregation and online re-parameterization. The LADH-OBB module decouples classification and regression tasks, improving the precision of rotation angle regression. To further adapt to resource-constrained environments, this study incorporates the Taylor Pruning algorithm, significantly reducing model parameters and computational costs. Experimental results on the RotBar dataset demonstrate the superior performance of EA-OBB, achieving an optimal balance of precision, recall, and computational complexity compared to existing methods.
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