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| Barcodes have played an essential role in our daily life. Localizing, or detecting them in real scenes in a fast and robust way has many practical applications. Recently, some deep learning-based methods have shown great potential in object detection. However, because barcodes are placed at any angle, vertical bounding boxes cannot sufficiently capture accurate orientation and scale information. In this paper, we propose a barcode detector that performs dense prediction to accurately locate the position of pixels belonging to the barcode region. For better detection performance, we design a spatial attention module to integrate global information adaptively, which can be easily plugged into the prediction backbone. Meanwhile, we employ the knowledge distillation training strategy to train a small student network with the help of a heavy teacher network. Extensive experiment results demonstrate that our method can perform real-time speed on CPU environments and locate barcodes in images with complex scenes. |
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| Keywords: Computer vision; Image process; Barcode detection |
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