Cargo positioning and sorting model based on deep learning and sampling evaluation

Due to the explosive growth of e-commerce, logistics centers are experiencing low efficiency in locating and sorting goods, so traditional manual processing is gradually transforming to automation and intelligence. Thus, by combining feature pyramid networks with deep image data, this study suggests a cargo positioning and sorting model that greatly enhances the classification and location of soft and deformable goods. Simultaneously, the robot grasping position selection is improved to enhance the accuracy and efficiency of sorting. The experimental results of the model indicated that the AP value achieved by the network was about 91.9%, while the area value of the offline curve was about 94.8%. The model realized fast and high-precision goods sorting in a dynamic environment, which effectively reduces the manpower dependence and operational errors, and provides a practical solution to improve the responsiveness and processing capability of the supply chain.