Binocular automatic particle inspection machine for bottled medical liquid examination

Fang, Jifei; Wang, Yaonan; Wu, Chengzhong

doi:10.1109/cac.2013.6775767

Cited by 4 publications

(1 citation statement)

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“…In terms of bottle recognition, Muresan et al extracted the bottle information via segments, identified the interested text area and converted the obtained information to human-readable characters [10]. Fang et al proposed a binocular inspection algorithm to overcome impurities in ampoules and penicillin bottles [11]. Thiyagarjan et al firstly collected the logo of bottles via a camera, then classified the obtained logo to recognize bottles [12].…”

Section: Bottle Classificationsmentioning

confidence: 99%

A Serial Attention Frame for Multi-Label Waste Bottle Classification

Xiao

Xu²,

Tian

et al. 2022

Applied Sciences

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The multi-label recognition of damaged waste bottles has important significance in environmental protection. However, most of the previous methods are known for their poor performance, especially in regards to damaged waste bottle classification. In this paper, we propose the use of a serial attention frame (SAF) to overcome the mentioned drawback. The proposed network architecture includes the following three parts: a residual learning block (RB), a mixed attention block (MAB), and a self-attention block (SAB). The RB uses ResNet to pretrain the SAF to extract more detailed information. To address the effect of the complex background of waste bottle recognition, a serial attention mechanism containing MAB and SAB is presented. MAB is used to extract more salient category information via the simultaneous use of spatial attention and channel attention. SAB exploits the obtained features and its parameters to enable the diverse features to improve the classification results of waste bottles. The experimental results demonstrate that our proposed model exhibited good recognition performance in the collected waste bottle datasets, with eight labels of three classifications, i.e., the color, whether the bottle was damage, and whether the wrapper had been removed, as well as public image classification datasets.

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Section: Bottle Classificationsmentioning

confidence: 99%