Weiwei Cai1,2, Yaping Song1, Huan Duan1, Zhenwei Xia1, Zhanguo Wei1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1539-1555, 2022, DOI:10.32604/cmes.2022.019785
Abstract In the smart logistics industry, unmanned forklifts that intelligently identify logistics pallets can improve work
efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.
Therefore, they play a critical role in smart warehousing, and semantics segmentation is an effective method to
realize the intelligent identification of logistics pallets. However, most current recognition algorithms are ineffective
due to the diverse types of pallets, their complex shapes, frequent blockades in production environments, and
changing lighting conditions. This paper proposes a novel multi-feature fusion-guided multiscale bidirectional
attention (MFMBA) neural network for logistics pallet segmentation. To better predict… More >
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