Design and optimization of intelligent logistics system based on machine vision and SSD- MobileNetV2 model

Authors

  • Jun Chen School of Economics and Management. Xihang University. 259, West 2nd Ring Road - 711077 Xi’an City, Shaanxi Province (China). Author
  • Lan Jiang School of Economics and Management. Xihang University. 259, West 2nd Ring Road - 711077 Xi’an City, Shaanxi Province (China). Author

DOI:

https://doi.org/10.52152/6s7pdx13

Keywords:

machine vision, SSD, MobileNetV2, intelligent, logistics, Sorting, Design, Optimization, System design, Mechanical.

Abstract

he logistics and transportation indicators, reduce the occurrence of misuse, and improve the operation efficiency of the system. In this paper, we use MobileNetV2 and SSD (Single Engine, MultiBox Detector) to build the operating data of an intelligent logistics system, and use the multi-signal data simplification of SSD to minimize transportation, sorting, and sorting, and integrate with MobileNetV2. Firstly, the MobileNetV2 method is used to judge the pixel and graphic data in logistics, simplify the data complexity, and determine the key points of the data.The results show that the compliant transportation, speed adjustment, cross-data transmission, transportation data, selection time, system occupancy and multi-logistics tasks of the SSD-MobileNetV2 model are 0.12, 0.59, 0.47, 0.68, 0.64, 0.36, 0.78 and 0.67, which are significantly better than those of 0.14, 0.44, 0.32, 0.41, 0.44, 0.11, 0.15 and 0.27 indicate that the method proposed in this paper can significantly optimize the logistics index. In terms of transportation distance, the comprehensive rates of SSD, MobileNetV2, Manual statistics and SSD-MobileNetV2 methods were 35.36%, 22.08%, 30.12% and 42.75%, respectively, indicating that the SSD-MobileNetV2 method had the best optimization effect. It can be seen that SSD MobileNetV2 can optimize the intelligent logistics system and improve the operation effect.

Published

2025-09-15

Issue

Section

Research articles