FPAO: garbage classification via a multilayer network based on feature preference and attention optimization

Shuo Zhang

doi:10.1117/12.3053026

3 January 2025 FPAO: garbage classification via a multilayer network based on feature preference and attention optimization

Shuo Zhang

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 1344203 (2025) https://doi.org/10.1117/12.3053026
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

Automatic garbage classification has provided a great opportunity for the development of cities due to the increasing environmental concerns and the need for effective waste management. Efficient and intelligent garbage sorting technology is appealing for facilely classifying and detecting different types of garbage from images or videos to facilitate recycling and proper disposal. This paper aims to propose a novel multi-layer network called FPAO based on feature preference and attention optimization to achieve automatic garbage classification from both images and surveillance videos. It is mainly designed with a convolutional layer to carry the feature extraction operation, a pooling layer to conduct the down sampling, a bidirectional long short-term memory (BiLSTM) layer to capture the temporal correspondences and preference, and an attention optimization layer to adaptively assign important feature weight to enhance the accuracy of the classification detection. Experimental evaluation indicates that the proposed FPAO exhibits superior performance in detection stability and robustness. The qualitative and quantitative comparison demonstrates that FPAO yields competitive detection results and outperforms existing similar methods.

Citation Download Citation

Shuo Zhang "FPAO: garbage classification via a multilayer network based on feature preference and attention optimization", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 1344203 (3 January 2025); https://doi.org/10.1117/12.3053026

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KEYWORDS

Video

Image classification

Object detection

Video surveillance

Feature extraction

Matrices

Convolution

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