An Automatic Garbage Classification System Based on Deep Learning

Kang, Zhuang; Yang, Jie; Li, Guilan; Zhang, Zeyi

doi:10.1109/access.2020.3010496

Cited by 83 publications

(30 citation statements)

References 23 publications

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“…A prediction model trained using garbage data can be utilized to execute garbage recognition and classification tasks. However, the existing classification algorithms run on high-performance servers or PCs and do not satisfy the actual demands of garbage classification systems [10,11,12,13]. Therefore, realizing a highly precise and efficient classification system that satisfies actual realworld demands remains a yet-unsolved challenge.…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

Wei

et al. 2021

IEEE Access

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The dramatic increase in the amount of garbage and complex diversity of the materials in the garbage bring serious environmental pollution problems and wastes resources. Recycling reduces waste but manual pipeline waste sorting involves a harsh working environment at high labor intensity with low sorting efficiency. In our paper, a novel intelligent garbage classification system based on deep learning and an embedded Linux system is proposed. The system is divided into three parts. First, a Raspberry Pi 4B is utilized as the master board for the hardware system. The peripherals of the system consist of a touch panel, sensors, a 2-DOF (degree of freedom) servo, and a camera. Second, a new GNet model for garbage classification based on transfer learning and the improved MobileNetV3 model is proposed. Third, a GUI based on Python and QT is employed to build a human-computer interaction system to facilitate system manipulation and observation. A series of garbage classification experiments on the Huawei Garbage Classification Challenge Cup dataset were conducted. The proposed classification system's prediction accuracy was 92.62% at 0.63 s efficiency. The experimental results in this paper demonstrate that the proposed intelligent garbage classification system delivers high performance both in terms of accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

Wei

et al. 2021

IEEE Access

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“…Rabano et al [16] developed a garbage classification model that can be deployed on the Android system to sort glass, paper, cardboard, plastic, metal, and other garbage. Kang et al [17] developed a garbage classification model based on the ResNet-34 algorithm, classification accuracy reached 99%.…”

Section: Introduction mentioning

confidence: 99%

Cost-effective method for degradability identification of MSW using convolutional neural network for on-site composting

Huang

Dai

et al. 2021

International Journal of Agricultural and Biological Engineering

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Automatically identifying the degradability of municipal solid waste (MSW) is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes. In this study, a cost-effective method was proposed for the degradability identification of MSW. Firstly, the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site. Secondly, a lite convolutional neural network (CNN) model was built with only 3.37 million parameters, and then a total of eight models were trained on these datasets with and without the image augmentation operations, respectively. Finally, a degradability identification system was built for on-site composting, where the images were cut to different sizes of small squares for prediction, and the experiments were conducted to find the best combinations of the trained models and the cutting size. The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher, and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%, which indicated the capability of this cost-effective method to identify the degradability of MSW.

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“…With the acceleration of urbanization and the boom of the urban population, the dilemma of "urban garbage siege" has become one of the environmental pollution problems in cities. According to the latest report of International Lianhe Zaobao, the global garbage volume will increase by 70% by 2050 [1]. In this context, how to achieve the reduction, recycling and harmlessness of urban garbage has become an important issue for urban management.…”

Section: Introductionmentioning

confidence: 99%

“…Ramalingam et al [13] proposed a method for detecting and locating pavement defects and garbage using deep convolutional neural network. Based on ResNet-34 algorithm, Kang et al [14] proposed a garbage classification algorithm. They also further optimized the network structure of the algorithm from the multi feature fusion of input images, the feature reuse of the residual unit, and the design of a new activation function.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Model for Pavement Garbage Classification Based on Deep Learning

2021

IJOMAM

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In response to the requirement of urban environmental health to classify and recycle citizens' garbage, a lightweight model for pavement garbage classification based on deep learning is proposed considering the powerful performance of computer convolutional neural networks in image classification. In order to reduce the scale of the network model to run better on embedded devices, on the basis of the original convolutional neural network framework, the ghost module is reasonably embedded into the lightweight model for pavement garbage classification and the ordinary convolution layers in the path aggregation network (PANet) are replaced by the depthwise separable convolution layers. In order to improve accuracy and real-time performance, the attention mechanism squeeze and excitation layer (SELayer) is embedded to fuse more spatial features. Then, a self-made dataset is built through internet search and manual photography. The self-made dataset contains seven types of common pavement garbage images. Through experimental demonstration, the proposed model can well locate the garbage and identify the types of garbage. And the proposed model has the advantage of small size. The proposed model also achieves 15.58 frames per second (FPS) real-time performance, and the size of the model is only 5.18 mbyte (MB). The FPS increases by 27.5%. The model size is reduced by about two thirds.

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An Automatic Garbage Classification System Based on Deep Learning

Cited by 83 publications

References 23 publications

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

Cost-effective method for degradability identification of MSW using convolutional neural network for on-site composting

A Lightweight Model for Pavement Garbage Classification Based on Deep Learning

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