Effective Barcode Hunter via Semantic Segmentation in the Wild

Barcodes are popularly used for product identification in many scenarios. However, locating them on product images is challenging. Half-occlusion, distortion, darkness or targets being too small to recognize can often add to the difficulties using conventional methods. In this paper, we introduce a large-scale diverse barcode dataset and adopt a deep learning-based semantic segmentation approach to address these problems. Specifically, we use an efficient method to synthesize 30000 well-annotated images containing diverse barcode labels, and get Barcode-30 k, a large-scale dataset with accurate pixel-level annotated barcode in the wild. Moreover, to locate barcode more precisely, we further propose an Effective Barcode Hunter - BarcodeNet. It is a semantic segmentation model based on CNN (Convolutional Neural Network) and is mainly formed with two novel modules, Prior Pyramid Pooling Module (P3M) and Pyramid Refine Module (PRM). Additional ablation studies further demonstrate the effectiveness of BarcodeNet, and it yields a high mIoU result of 95.36% on the proposed synthetic Barcode-30 k validation-set. To prove the practical value of the whole system, we test the BarcodeNet trained on train-set of Barcode-30 k on a manually annotated testing set that only collected from cameras, it achieves mIoU of 90.3%, which is a very accurate result for practical applications.