Zhang Xin, Shao Pengfei
College of Business, Jiaxing University, Jiaxing, 314001 Zhejiang, China.
Zhejiang Wanli University, Ningbo, 315100 Zhejiang, China.
Appl Bionics Biomech. 2022 Aug 29;2022:2949216. doi: 10.1155/2022/2949216. eCollection 2022.
Logistics migration and movement require precise information updates for traceability and visibility of goods through E-commerce platforms. Computer vision and digital image processing techniques are used for visual identification and tracking through different warehouses and delivery points. In this article, an incessant visualized tracking scheme (IVTS) is designed for identifying and tracking E-commerce logistics throughout the migration points. This scheme endorsed computer vision technology for logistics recognition and labelled data detection. In this scheme, the labelled logistics data is verified for its similarity in different migrating locations and to the endpoint. Based on the dimensional features and regional-pixel similarity factor, it is verified using the deep neural network. This learning process identifies dimensional variations due to logistics displacement and position suppressing the similarity variations. It is performed based on the migration and information available to prevent tracking errors. For the varying locations and logistics displacement, the error pixel regions are identified and trained for possible similarity detection. The proposed scheme effectively improves visual accuracy, tracking maximization, and logistics detection by reducing dimensional errors.
物流迁移和移动需要通过电子商务平台进行精确的信息更新，以实现货物的可追溯性和可视性。计算机视觉和数字图像处理技术用于在不同仓库和配送点进行视觉识别和跟踪。在本文中，设计了一种连续可视化跟踪方案（IVTS），用于在整个迁移点识别和跟踪电子商务物流。该方案认可使用计算机视觉技术进行物流识别和标记数据检测。在该方案中，对标记的物流数据在不同迁移位置以及到终点的相似性进行验证。基于尺寸特征和区域像素相似性因子，使用深度神经网络进行验证。该学习过程识别由于物流位移和位置导致的尺寸变化，抑制相似性变化。它基于可用的迁移和信息来执行，以防止跟踪错误。对于不同的位置和物流位移，识别错误像素区域并进行训练以进行可能的相似性检测。所提出的方案通过减少尺寸误差有效地提高了视觉准确性、跟踪最大化和物流检测。
