Garbage Classification System with YOLOV5 Based on Image Recognition

Yang, Guanhao; Jin, Jintao; Lei, Qujiang; Wang, Yi; Zhou, Jingzhe; Sun, Zhe; Li, Xiuhao

doi:10.1109/icsip52628.2021.9688725

Cited by 21 publications

(11 citation statements)

References 2 publications

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“…YOLO's outstanding performance in object recognition is improved over many versions, and the model is now widely used in garbage classification. Specifically, the effectiveness of YOLO3 for trash classification has been reported in publications such as [13][14][15], and YOLO5 appears in works such as [16,17].…”

Section: Related Workmentioning

confidence: 97%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The deep learning-based ResNet-50 model, an improved version of the ResNet model, is used to classify trash from the dataset of trash images. The experimental results, which are tested both on the dataset and in real time, show that ResNet-50 had an average accuracy of 96%, higher than that of related models. Moreover, integrating the classification module into a Raspberry Pi computer, which controlled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste, created a complete trash classification system. This proves the efficiency and high applicability of the proposed system.

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Section: Related Workmentioning

confidence: 97%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Some applications are shown in Table . The YOLO series could achieve satisfying performance. ,,− Moreover, practices manifested a startling detection speed for YOLO, which always achieved real-time. For example, YOLOv4 reached a speed 24% higher than faster R-CNN, while the mAP was 2.03% higher .…”

Section: Development and Status Quo Of Sensor-based Waste Sorting Tec...mentioning

confidence: 99%

“…71 Without material innovations, YOLOv5 extremely improves the detection speed to a startling 140 fps, while accuracy is not sacrificed. 13,72 YOLOX: This backs YOLO to be anchor-free. Based on YOLOv3, YOLOX applies DarkNet-53 and FPN to extract features and fuse various-scaled features.…”

Section: 21ab Applications In Solid Wastementioning

confidence: 99%

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Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

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The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

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“…The deep learning model that performed best in the tests had 300 epochs and 96.5% accuracy. In order to more effectively deploy face mask identification in the real world, especially when monitoring mask dress-up in public areas, research by Yang et al [21] suggest replacing manual face mask detection with a (YOLOV5) method. The experimental findings demonstrate that the proposed algorithm in this research can successfully identify face masks in public areas.…”

Section: Literature Surveymentioning

confidence: 99%

Incorrect facemask-wearing detection using image processing and deep learning

Habeeb

2023

Bulletin EEI

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Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.

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Garbage Classification System with YOLOV5 Based on Image Recognition

Cited by 21 publications

References 2 publications

A Deep Trash Classification Model on Raspberry Pi 4

A Deep Trash Classification Model on Raspberry Pi 4

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Incorrect facemask-wearing detection using image processing and deep learning

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