An Assisted Forklift Pallet Detection with Adaptive Structure Feature Algorithm for Automated Storage and Retrieval Systems

Syu, Jia-Liang; Li, Hsin-Ting; Chiang, Jen‐Shiun; Hsia, Chih-Hsien; Wu, Po-Han; Hsieh, Chi-Fang

doi:10.1007/978-981-10-3187-8_26

Cited by 1 publication

(3 citation statements)

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“…Furthermore, all experiments were carried out in the same environment and with the same hyperparameters. We compared AlexNet [18], Res-Net [19], DenseNet [20], Unet [21], and DeepLab-v3 [2] with the proposed MFMBA model. The comparative experimental results of various methods are shown in Tables 2 and 3 and Fig.…”

Section: Experimental Results Of Different Methodsmentioning

confidence: 99%

“…Only 1% and 20% of the training samples were chosen in separate experiments. [36] 0.9188 0.8602 0.8885 DenseNet [37] 0.8487 0.9376 0.8910 Unet [38] 0.8497 0.8915 0.8579 DeepLab-v3 [39] 0.8512 0.9013 0.8755 MFMBA (Proposed method) 0.9237 0.9417 0.9275 [19] 0.9517 0.9675 0.9595 DenseNet [20] 0.8917 0.9580 0.9478 Unet [21] 0.9258 0.9389 0.9524 DeepLab-v3 [22] 0.9387 0.9221 0.9599 MFMBA (Proposed method) 0.9564 0.9793 0.9626…”

Section: Experimental Results Of Different Methodsmentioning

confidence: 99%

“…However, these colour-based approaches are vulnerable to interference from non-simple backdrops. The Haar-based Adaboost approach, according to Syu et al [19], uses the AS-for-pallets algorithm to detect pallets. In addition, Seelinger et al [20] presented mobile camera space manipulation (MCSM), a visual guiding control system to help forklift drivers.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

Cai¹,

Song²,

Duan³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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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 the foreground category (the pallet) and the background category (the cargo) of a pallet image, our approach extracts three types of features (grayscale, texture, and Hue, Saturation, Value features) and fuses them. The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual, complex environment, which usually makes feature extraction difficult. Our study proposes a multiscale architecture that can extract additional semantic features. Also, since a traditional attention mechanism only assigns attention rights from a single direction, we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions, horizontally and vertically, significantly improving segmentation. Finally, comparative experimental results show that the precision of the proposed algorithm is 0.53%-8.77% better than that of other methods we compared.

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