Smart Waste Management and Classification Systems Using Cutting Edge Approach
Abstract
:1. Introduction
- We combine IoT and deep learning paradigms to ensure an optimal solution for waste management.
- We design a waste classification model (WCM) that classifies the waste into biodegradable and non-biodegradable items, such as plastic, metal, glass, etc., using the image classification technique.
- We implement an architectural development process of smart waste dump using the segmented grid image captured by a camera mounted on the raspberry pie.
- We develop a smart way to monitor the waste dump in real time using a cutting-edge approach that decreases the overall latency and improves energy utilization.
- We combine the cloud and edge processing mechanism as a hybrid computing phenomenon which improves the overall performance of the proposed system.
- We perform a performance analysis of the proposed system results.
2. Literature Review
3. Proposed Methodology
3.1. Edge Node Processing
3.1.1. Image Capture
3.1.2. Grid Segmentation
- Phase 1: In the first step, we map the captured image of a waste dump on a grid-like cell structure, i.e., 5 × 6 matrix, of the same resolutions, and convert it into grayscale. The size of the grid and the total number of cells depend on the test image size.
- Phase 2: In the second step, initial classification is performed, and labels are applied based on the texture, color, and position features of each segment by the VGG-16 algorithm as dicussed in Section 3.3.2. Each cell segment is processed separately to recognize the waste items appropriately. After that, we move from one segment to another column-wise to the VGG16 algorithm to recognize the waste item effectively. Each cell contains one waste object at a time, which is ultimately picked by the robotic arm and placed in a respective bin. This process continues until the whole test image has been processed. Incorrect segments are put back, and final labeling or classification consists of the union of only correct segments.
3.1.3. Waste Item Classification
3.2. Control Unit
3.3. Cloud Processing
3.3.1. Data Storage
3.3.2. Deep Learning Algorithm
4. Experimental Setup
4.1. Dataset Description
4.2. Performance Metrics
4.2.1. Accuracy
4.2.2. Latency Overhead
4.2.3. Resource Energy Utilization
4.3. Performance Analysis
Accuracy Comparison
4.4. System Latency Comparison
4.5. Error Rate Comparison
4.6. Inference Time Comparison
4.7. Average Precision Comparison
4.8. Resource Utilization Comparison
5. Critical Analysis
Test Predictions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study | Architecture | For IoT | Waste Category | Hardware Category | Accuracy (%) | Dataset | Remarks |
---|---|---|---|---|---|---|---|
[74,75] | Multivariate analysis with neural network | NA | Light Weight Metals | Weight sensor, linear laser, 3D camera | 85% | NA | Cannot classify other materials |
[76] | KNN | NA | Recyclable Paper | Camera | 93% | NA | Require consistent lightening during identification phase |
[77] | Hu’s image invariant moments with KNN | NA | Inorganic material such as bottles, cutlery, Cans | Camera | 98% | NA | Unreliable results due to small dataset |
[78] | DNA Computing Algorithms (RGBI) | NA | Paper | Camera | 95.1% | NA | Require consistent lightening during identification phase |
[79] | SVM and CNN | NA | Paper, Metal and Plastic | Camera | SVM: 94.8% CNN: 83% | NA | Dataset is less versatile due to Low GPU memory images scaled down from 256 × 256 to 32 × 32 |
[80] | Fast-R CNN | NA | Plastic Bottles | Robotic Arm, KUKA, Camera | 91% | NA | Segregate only plastic. Output reliant on lighting |
[81] | Scale Invariant Feature Transform (SIFT) | NA | Organic and inorganic items with product labels | Camera | 89.9% | Self-made dataset of 192 images | Most waste items do not have product labels; hence, the scope is limited |
Study | Architecture | For IoT | Waste Category | Hardware Category | Accuracy (%) | Dataset | Remarks |
---|---|---|---|---|---|---|---|
[52] | VGG16, CNN, ResNet50 | NA | Household food waste, recyclable waste, hazardous waste, residual waste | NA | VGG16: 37% CNN: 37% ResNet-50: 47% | Self-made | Not integrated with a decision support or classification system. Accuracy is not properly calculated |
[82] | YOLOv3, Darknet neural network | NA | Glass, paper, metal, plastic, cardboard, organic waste | NA | Glass: 97% paper: 85% metal: 99% plastic: 91% cardboard: 97% org-waste: 98% mAP: 94% | Self-made | Takes more time to detect an object. |
[83] | WasNet, Lightweight neural network | NA | NA | Camera | ImageNet: 64.5% Garbage Classification: 82.5% TrashNet: 96.10% | ImageNet, Garbage Classification dataset, TrashNet | Implementation and results are unreliable. |
[73] | SURF algo., KNN | Yes | Metal, Plastic | Ultrasonic sensor, Moister sensor, metal sensor, Camera | 95% | Self Made | Only one waste item can be put in dustbin at a time. If image of waste material captured in low light then results may vary. |
[57] | CNN, Self-Learning Neural Network | NA | Plastic, Paper, Cardboard, Metal | NA | 76% | Trash Net | Less accuracy due to limited dataset. Not integrated with a decision support or classification system. |
[84] | CNN, Image Processing Algo. | NA | Plastic and its four categorical: PS, PP, PE-HD, PET | Camera, Airjet | 74% | WaDaBa | Less effective as compared to other CNNs. |
[62] | ResNet50, DenseNet169, VGG16, AlexNet | NA | Glass, paper, metal, plastic, cardboard, Trash | NA | ResNet50: 89.7% DenseNet169: 92.6% VGG16: 86.9% AlexNet: 83.7% | ImageNet | System misclassify ‘glass’. Not integrated with a decision support or classification system. |
Module Name | Hardware Requirement | Component Specs |
---|---|---|
Edge Node | Raspberry Pi 4B Pi-CAM | 4GB (RAM) 5MP |
Cloud Processing | Artificial Intelligence Module | — |
Pre-trained Deep Learning Model | — | |
Control Unit | Micro-controller Bluetooth Module Gripper Stepper Motor | Arduino Mega 2560 HC 05 (Slave Mode) 4 Degree of Freedom (DOF) 1.8 deg/step, 2.4 V |
Power Source | DC Battery | 6V, 4.5AH |
Class Label | Training | Testing | Total |
---|---|---|---|
Metal | 328 | 82 | 410 |
Metal | 328 | 82 | 410 |
Glass | 400 | 101 | 501 |
Plastic | 385 | 97 | 482 |
Trash | 109 | 28 | 137 |
Total | 1222 | 308 | 1530 |
Study | Architecture | Waste Category | Accuracy |
---|---|---|---|
[52] | VGG16, CNN, ResNet50 | Household food waste, recyclable waste, hazardous waste, residual waste | VGG16:37% CNN: 37% ResNet-50: 47% |
[83] | WasNet, Lightweight neural network | Not Available | ImageNet: 64.5% Garbage classification: 82.5% TrashNet: 96.10% |
[57] | CNN, Self-Learning Neural Network | Plastic, Paper, Cardboard, Metal | 76% |
[84] | CNN, Image-Processing Algorithm | Plastic and its four categories PS, PP, PE-HD PET | 74% |
[62] | ResNet, DenseNet VGG-16, AlexNet | Glass, paper metal, plastic cardboard, trash | ResNet: 89.7% DenseNet169: 92.6% VGG16: 86.9% AlexNet: 83.7% |
Proposed | VGG-16, Fast R-CNN, MobileNetV2 | Glass, plastic metal, trash | VGG16: 96.1% Fast RCNN: 88% MobileNet V2: 85% |
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Cheema, S.M.; Hannan, A.; Pires, I.M. Smart Waste Management and Classification Systems Using Cutting Edge Approach. Sustainability 2022, 14, 10226. https://doi.org/10.3390/su141610226
Cheema SM, Hannan A, Pires IM. Smart Waste Management and Classification Systems Using Cutting Edge Approach. Sustainability. 2022; 14(16):10226. https://doi.org/10.3390/su141610226
Chicago/Turabian StyleCheema, Sehrish Munawar, Abdul Hannan, and Ivan Miguel Pires. 2022. "Smart Waste Management and Classification Systems Using Cutting Edge Approach" Sustainability 14, no. 16: 10226. https://doi.org/10.3390/su141610226
APA StyleCheema, S. M., Hannan, A., & Pires, I. M. (2022). Smart Waste Management and Classification Systems Using Cutting Edge Approach. Sustainability, 14(16), 10226. https://doi.org/10.3390/su141610226