Out-of-focus blurring of the QR code is very common in mobile Internet systems, which often causes failure of authentication as a result of a misreading of the information hence adversely affects the operation of the system. To tackle this difficulty, this work firstly introduced an edge prior information, which is the average distance between the center point and the edge of the clear QR code images in the same batch. It is motivated by the theoretical analysis and the practical observation of the theory of CMOS image sensing, optics information, blur invariants, and the invariance of the center of the diffuse light spots. After obtaining the edge prior information, combining the iterative image and the center point of the binary image, the proposed method can accurately estimate the parameter of the out-of-focus blur kernel. Furthermore, we obtain the sharp image by Wiener filter, a non-blind image deblurring algorithm. By this, it avoids excessive redundant calculations. Experimental results validate that the proposed method has great practical utility in terms of deblurring quality, robustness, and computational efficiency, which is suitable for barcode application systems, e.g., warehouse, logistics, and automated production.

As the fundamental element of the Internet of Things, the QR code has become increasingly crucial for connecting online and offline services. Concerning e-commerce and logistics, we mainly focus on how to identify QR codes quickly and accurately. An adaptive binarization approach is proposed to solve the problem of uneven illumination in warehouse automatic sorting systems. Guided by cognitive modeling, we adaptively select the block window of the QR code for robust binarization under uneven illumination. The proposed method can eliminate the impact of uneven illumination of QR codes effectively whilst meeting the real-time needs in the automatic warehouse sorting. Experimental results have demonstrated the superiority of the proposed approach when benchmarked with several state-of-the-art methods.

Targeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved and optimized battery electrode defect detection model based on YOLOv8. Firstly, the lightweight GhostCony is used to replace the standard convolution, and the GhostC2f module is designed to replace part of the C2f, which reduces model computation and improves feature expression performance. Then, the coordinate attention (CA) module is incorporated into the neck network, amplifying the feature extraction efficiency of the improved model. Finally, the EIoU loss function is employed to swap out the initial YOLOv8 loss function, which improves the regression performance of the network. The empirical findings demonstrate that the enhanced model exhibits increments in crucial performance metrics relative to the original model: the precision rate is elevated by 2.4%, the recall rate by 2.3%, and the mean average precision (mAP) by 1.4%. The enhanced model demonstrates a marked enhancement in the frames per second (FPS) detection rate, significantly outperforming other comparative models. This evidence indicates that the enhanced model aligns well with the requirements of industrial development, demonstrating substantial practical value in industrial applications.

With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enterprise anti-counterfeiting certification. Based on information hiding technology, the proposed approach can solve these challenging problems and provide a low-cost, difficult to forge, and easy to identify solution, while achieving the function of conventional 2D barcodes. By weighting between the perceptual quality and decoding robustness in sensing recognition, the customized 2D barcode can maintain a better aesthetic appearance for anti-counterfeiting and achieve fast encoding. A new picture-embedding scheme was designed to consider 2D barcode, within a unit image block as a basic encoding unit, where the 2D barcode finder patterns were embedded after encoding. Experimental results demonstrated that the proposed customized barcode could provide better encoding characteristics, while maintaining better decoding robustness than several state-of-the-art methods. Additionally, as a closed source 2D barcode that could be visually anti-counterfeit, the customized 2D barcode could effectively prevent counterfeiting that replicate physical labels. Benefitting from the high-security, high information capacity, and low-cost, the proposed customized 2D barcode with sensing recognition scheme provide a highly practical, valuable in terms of marketing, and anti-counterfeiting traceable solution for future smart IoT applications.

With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the existing 2D barcode cannot be changed once it is printed, it can only be replaced with more expensive radio frequency identification (RFID) labels or new 2D barcodes, causing a waste of human resources and costs. For better circulation efficiency and resource utilization, we propose a new design of the rewritable and sustainable 2D barcode based on the fault-tolerance mechanism. The ability to add new information in the 2D barcode can be achieved through data encryption and the insertion of a rewritable layer. It means the message of 2D barcodes could be changed, and increases the flexibility and liquidity of the 2D barcode application. Besides, the encoding and decoding method of the proposed 2D barcode is presented. Experimental results have illustrated the superiority of rewritable and sustainable 2D barcodes in the traceability of herbal medicine compared with the conventional 2D barcodes, and demonstrated the feasibility of the design. The findings show the potential for significant application in the field of traceability in smart IoT, as well as in the manufacturing industry and logistics.