Smartphone-based bulky waste classification using convolutional neural networks

Wang, Hanxiang; Li, Yanfen; Dang, L. Minh; Ko, Jae-Sung; Han, Dongil; Moon, Hyeonjoon

doi:10.1007/s11042-020-09571-5

Cited by 18 publications

(8 citation statements)

References 28 publications

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“…Considering that the existing detectors cannot extract adequate features at precise target areas, some researchers deepen and broaden network structures to improve the efficiency of feature extraction (Dang et al, 2021;H. Wang et al, 2020;Q.…”

Section: Related Workmentioning

confidence: 99%

“…But it is challenging for the existing extractors to precisely obtain complete target areas, especially when processing images with complex backgrounds. To make the model pay more attention to the objective region, a great effort has been put into acquiring more valuable features by deepening and broadening feature extractors in previous studies (Dang et al, 2021;H. Wang et al, 2020;Q.…”

Section: Introductionmentioning

confidence: 99%

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Attention‐guided multiscale neural network for defect detection in sewer pipelines

Wang

Dang

et al. 2023

Computer aided Civil Eng

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Sanitary sewer systems are major infrastructures in every modern city, which are essential in protecting water pollution and preventing urban waterlogging. Since the conditions of sewer systems continuously deteriorate over time due to various defects and extrinsic factors, early intervention in the defects is necessary to prolong the service life of the pipelines. However, prior works for defect inspection are limited by accuracy, efficiency, and economic cost. In addition, the current loss functions in object detection approaches are unable to handle the imbalanced data well. To address the above drawbacks, this paper proposes an automatic defect detection framework that accurately identifies and localizes eight types of defects in closed‐circuit television videos based on a deep neural network. First, an effective attention module is introduced and used in the backbone of the detector for better feature extraction. Then, a novel feature fusion mechanism is presented in the neck to alleviate the problem of feature dilution. After that, an efficient loss function that can reasonably adjust the weight of training samples is proposed to tackle the imbalanced data problem. Also, a publicly available dataset is provided for defect detection tasks. The proposed detection framework is robust against the imbalanced data and achieves a state‐of‐the‐art mean average precision of 73.4%, which is potentially applied in realistic sewer defect inspections.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attention‐guided multiscale neural network for defect detection in sewer pipelines

Wang

Dang

et al. 2023

Computer aided Civil Eng

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“…Due to the recent advancements in ML, both the scientific community and industry have attempted to apply ML-based pattern recognition in various areas, such as agriculture [ 32 ], resource management [ 33 ], and construction [ 34 ]. At present, many types of defect classification algorithms have been presented for both binary and multi-class classification tasks.…”

Section: Defect Inspectionmentioning

confidence: 99%

Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey

Wang

Dang

et al. 2022

Sensors

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Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed.

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“…Several studies used three CNN architectures for classification: MobileNetV2 had a classification accuracy of 96.27 percent, ResNet34 96.273 percent, DenseNet121 96.42 percent [25] , VGG-19 86.19 percent, ResNet50 79.63 percent, and Inception-V3 81.15 percent [26].…”

Section: Related Workmentioning

confidence: 99%

Waste Classification using CNN Algorithm

Diqi

2022

ICoSTEC

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One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste efficiently. This paper uses the CNN algorithm to provide a problem-solving strategy to waste classification. The model achieves an accuracy of 0.9969 and a loss of 0.0205. As a result, we argue that employing CNN algorithms to categorize waste yields better results and reduces losses efficiently.

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Smartphone-based bulky waste classification using convolutional neural networks

Cited by 18 publications

References 28 publications

Attention‐guided multiscale neural network for defect detection in sewer pipelines

Attention‐guided multiscale neural network for defect detection in sewer pipelines

Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey

Waste Classification using CNN Algorithm

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