# A Serial Attention Frame for Multi-Label Waste Bottle Classification
###### Abstract  
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.  
m _Applied_  
_sciences_  
Aerial Attention Frame for Multi-Label Waste Bottle Classification  
2022121220222131 January 2  
abilities, they cannot define adjacent matrices and require higher computational costs. Object proposal methods (i.e., hypotheses-CNN-pooling (HCP) [4] and random crop pooling (RCP) [5]) generate object bounding box proposals, and then they extract useful information from these object proposals for image classification. Although they have a faster execution speed in comparison to traditional sliding windows, obtaining object proposals with these methods may be time consuming. Taking these factors into account, attention methods are presented [6; 7]. For instance, multi-class attentional regions (MCAR) [6] and the spatial regularization network (SRN) [7] used the current state to guide the previous state to extract salient features at a lower cost, in order to improve performance. However, most of the previous methods performed poorly, especially with regards to damaged waste bottle classification. Inspired by that, we propose a serial attention frame (SAF) to overcome the aforementioned drawback. This paper includes the following three parts: a residual learning block (RB), a mixed attention block (\(MAB\)), and a self-attention block (SAB). The RB uses 101-layer ResNet [8] to pretrain 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 channel attention [9] and spatial attention [9] in multi-label recognition. SAB exploits the obtained features and its parameters to enable the diverse features to obtain scores to improve the classification results of waste bottles. The contributions can be summarized as follows:  
1. A CNN is used to recognize waste bottles.
2. A serial attention frame, mainly including channel attention, spatial attention, and self-attention mechanisms, is used to extract the salient features of diverse types to improve the multi-label classification of waste bottles.
3. Image datasets of waste bottles are collected.  
The remainder of the paper is organized as follows: Section 2 discusses the related work. Section 3 reveals the architecture of our proposed model. The experiments are shown in Section 4. Section 5 is the conclusion.