Recyclable Waste Classification Using Computer Vision And Deep Learning

Ramsurrun, Nadish; Suddul, Geerish; Armoogum, Sandhya; Foogooa, Ravi

doi:10.1109/zinc52049.2021.9499291

Cited by 22 publications

(8 citation statements)

References 13 publications

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“…Writers in Reference 6 The paragraph outlines how computer vision and deep learning may be used to automatically recognize and classify solid trash into the following five groups: plastic, metal, paper, cardboard, and glass. An automatic recycling bin that opens its lid in accordance with the recognized trash type is part of the suggested system.…”

Section: Related Workmentioning

confidence: 99%

Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model

Malche,

Maheshwary,

Tiwari

et al. 2023

Trans Emerging Tel Tech

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Solid waste management is a significant challenge in the development of smart cities. Existing approaches for solid waste monitoring are often time‐consuming and resource intensive. Therefore, this study proposes a novel approach to solid waste monitoring that utilizes drone technology. The proposed method enables the efficient identification and classification of waste objects in the garbage discovered by the drone. This system can inspect every part of a smart city from a remote location, allowing for the timely and effective management of solid waste. Thus, the proposed system can be easily integrated in the existing waste management system for smart city. The drone‐based solid waste monitoring system comprises a drone equipped with a computer vision model for resource‐constrained devices and a software application that operates the drone and analyzes the captured image or video. The system utilizes the Internet of Things (IoT) to upload the collected data to the cloud, making it easily accessible whenever necessary. The proposed drone‐based solid waste monitoring system is a promising solution for the efficient and cost‐effective management of solid waste in smart cities. The system's innovative use of drone technology and IoT provides a scalable and adaptable solution that can be customized to meet the needs of any city.

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Section: Related Workmentioning

confidence: 99%

Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model

Malche,

Maheshwary,

Tiwari

et al. 2023

Trans Emerging Tel Tech

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show abstract

“…Some of the more difficult challenges in automated disassembly of LIBs batteries may be overcome with emerging advances in AI, computer vision, and robotic fundamentals. For example, one computer vision algorithm currently under development can identify various types of waste materials (Ramsurrun et al, 2021). It can also reliably track objects and guide the actions of robotic arms in cluttered scenes and complex conditions.…”

Section: Diagnostics For Automated Battery Disassemblymentioning

confidence: 99%

Technological options and design evolution for recycling spent lithium‐ion batteries: Impact, challenges, and opportunities

Hossain

Sarkar

Sahajwalla

2023

WIREs Energy & Environment

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The lithium‐ion battery (LIB) market is growing, driven by consumer demand and the imperative to reduce greenhouse gas (GHG) emissions. The socio‐environmental impacts of LIBs production are vast; thus, it is of paramount importance to acquire knowledge about the consequences of lithium metal mining on human health, farming and the overall ecosystem. The materials embedded in spent LIBs have high industrial value. To minimize environmental impacts and conserve declining natural resources, the global supply chain for the raw materials for LIBs should not rely solely on mining. Rather, manufacturers should have access to the critical materials recovered from waste, as a sustainable and reliable secondary source of valuable materials. It is also crucial to study the hazards and economic considerations associated with waste LIB management from disassembly to final recycling stage. This article evaluates and highlights various current approaches to recycling and reuse of LIBs, as well as potential future developments. The pros and cons of different technological options for recycling spent LIBs, and opportunities to use small‐scale recycling technology to overcome the associated barriers, are also discussed.This article is categorized under: Emerging Technologies > Energy Storage Emerging Technologies > Materials Policy and Economics > Research and Development

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“…We replicate state-of-the-art models for trash classification including RecycleNet [7], ResNet-50 [11], DNN-TC [17], RexNeXt-101 [18], and M-b Xception [19] to compare with the proposed model. Specifically, we utilized the ResNet-50 model and RecycleNet model with the same configurations, which were described in their work.…”

Section: Experiments Settingsmentioning

confidence: 99%

“…is approach is suitable for the problem of waste separation with material and object-based class divisions. Meanwhile, Ramsurrun et al [7] introduced a deep learning approach using computer vision to automatically identify the type of waste, which consists of an automated recycling bin. In 2016, Yang and ung [8] released the TrashNet dataset, which consists of six main classes (glass, paper, metal, plastic, cardboard, and trash), and some subsequent studies are based on this dataset.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning

Zhen

Liu

2022

Journal of Electrical and Computer Engineering

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Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model.

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Recyclable Waste Classification Using Computer Vision And Deep Learning

Cited by 22 publications

References 13 publications

Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model

Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model

Technological options and design evolution for recycling spent lithium‐ion batteries: Impact, challenges, and opportunities

A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning

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