Automatic waste sorting using shortwave infrared hyperspectral imaging system

Karaca, Ali Can; Ertürk, Alp; Güllü, M. Kemal; Elmas, M.; Ertürk, Sarp

doi:10.1109/whispers.2013.8080744

Cited by 22 publications

(19 citation statements)

References 11 publications

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“…There has been considerable research in the field of garbage collection. [5] proposed the use of a short-wave infrared hyperspectral imaging system for automatic waste sorting. In this system, waste is scanned by a short-wave infrared (SWIR) hyperspectral imaging system and is classified into six plastic waste types (polyethylene terephthalate, high density polyethylene, polyvinyl chloride, low density polyethylene, polypropylene, and polystyrene) as well as into paper, cardboard, metal, and glass.…”

Section: Existing Workmentioning

confidence: 99%

“…It is divided into recyclables, which refer to wastes suitable for recycling and resource utilization, including waste glass, metal, plastic, paper, fabrics, furniture, electrical and electronic products, and annual flowers; kitchen waste, which refers to perishable waste generated by households and individuals, including leftovers, leaves, peels, eggshells, tea residues, soup residues, bones, waste food and kitchen waste; hazardous waste, which refers to waste that directly or potentially harms human health or the natural environment and should be specially treated, including waste batteries and waste fluorescent tubes; and other garbage, which refers to domestic garbage such as diapers, dust, cigarette butts, disposable fast-food boxes, damaged flower pots and dishes, wallpaper, etc. Compared with the small data used in [5], [8], [12], [13], this dataset better fits the types of waste in real life scenarios, which increases the difficulty of model designing and training. We also used the TrashNet dataset for the development of our newly developed lightweight network.…”

Section: Datamentioning

confidence: 99%

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WasNet: A Neural Network-Based Garbage Collection Management System

Yang

2020

IEEE Access

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In response to the increasing pollution caused by unseparated garbage, classification systems for garbage separation have become very popular. First, we constructed a complex data augmentation combination for model training. Second, we designed a novel lightweight neural network garbage classification system called WasNet. This proposed network's 1.5 million parameters on the ImageNet dataset are onehalf of mainstream neural networks, while at 3 million floating point operations per second (FLOPs) it is one third of mainstream neural networks that have obtained the best performance among known lightweight neural networks. The accuracy on the ImageNet data set is 64.5%, on the Garbage Classification dataset it is 82.5%, and on the TrashNet dataset it is 96.10%. Furthermore, we transplanted the model to the hardware platform and assembled an intelligent trash can; we developed a garbage recognition application to facilitate users to directly identify and receive platform information; we built a visualization and decision support platform to help managers monitor traffic in real time. We combined the intelligent trash can, application, visualization and decision-making platform into a system, which is the most complete and effective system among the known research works. The results of the test we conducted on our platform using our extended dataset showed that our scheme is very reliable. At the same time, we also open source our extended datasets for use by other researchers.

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Section: Existing Workmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

WasNet: A Neural Network-Based Garbage Collection Management System

Yang

2020

IEEE Access

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“…In recent years, the development of remote sensing instruments, hyperspectral image (HSI) has become an emerging technology, and has been extensively used in the domains of geology [3], agriculture [4][5][6][7], global change [8], and national defense [9], with highly promising industrial potential [6,[10][11][12][13]. The hyperspectral sensor has 200 continuous spectral bands to enhance the spectral resolution.…”

Section: Introductionmentioning

confidence: 99%

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

et al. 2021

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Detection of surface defects on wet-blue leather is much more challenging than raw-hide leather. Since wet-blue leather turns blue and contains moisture after pre-treatment, it is a semi-product of the cowhide processing. At present, the defect detection of wet-blue leather is mostly carried out manually, and is time-consuming and labor-intensive for the professional inspectors. This paper is the first to use hyperspectral imaging (HSI) to implement the surface inspection of five wet-blue leather defects including brand masks, rotten grain, rupture, insect bites, and scratches in the pixel level detection. Hyperspectral Leather Defect Detection Algorithm (HLDDA) including Hyperspectral Target Detection (HTD) and Deep Learning (DL) techniques is proposed in this paper. In HTD, Weighted Background Suppression Constrained Energy Minimization (WBS-CEM) and WBS-Hierarchical CEM (WBS-hCEM) are developed in this paper by using weighting to suppress background and enhance the contrast between target and background. Experimental results show that the overall performance of WBS is better than the original CEM. In the DL part, 1D-Convolutional Neural Network (CNN), 2D-Unet and 3D-UNet architectures are designed to segment defect areas. For various characteristics of defects, 1D-CNN emphasizes defects with spectral features, 2D-Unet emphasizes defects with spatial features, and 3D-Unet can simultaneously process spatial and spectral information in HSI. Experimental results verified that the proposed HLDDA can effectively quantify and estimate the size of the defect, thereby accelerating the leather inspection process by professional inspectors and develop an automated leather grading towards Industry 4.0. INDEX TERMSConstrained Energy Minimization (CEM), Deep Learning (DL), Hyperspectral image (HSI)

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“…HSI is an emerging platform that combines traditional spectral and imaging techniques to obtain spectral and spatial information from samples [9]. Hyperspectral technology is widely used in geology and minerals, atmospheric sciences, oceans, agriculture, industrial production, and other fields [10][11][12]. Due to HSI information with the large spectral information, large frequency band, and high redundancy, the information processing of HSI is difficult.…”

Section: Introductionmentioning

confidence: 99%

Predicting the moisture content of Daqu with hyperspectral imaging

Chen

Tian

et al. 2020

International Journal of Food Engineering

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Daqu, a Chinese liquor fermentation starter, contains all kinds of microorganisms and enzymes for Chinese liquor fermentation. The moisture content of Daqu significantly influence on the reproduction of microorganisms in Daqu. This work presents for the first time that determination of moisture content of Daqu with hyperspectral imaging. The characteristic spectrum of water is extracted based on comparative experiments with varying moisture content. The molds based on the full bands and feature bands were established by the support vector regression (SVR) method, which is used to predict moisture content of Daqu during fermentation process. The performance of the model based on the feature bands (R2 = 0.9870, root mean square error (RMSE) = 0.0091) is comparable to the full bands and the dimensions of the spectral information were significantly reduced. This work presents a novel, rapid and nondestructive approach for detecting the moisture content in Daqu and lays a foundation for the application of hyperspectral imaging.

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Automatic waste sorting using shortwave infrared hyperspectral imaging system

Cited by 22 publications

References 11 publications

WasNet: A Neural Network-Based Garbage Collection Management System

WasNet: A Neural Network-Based Garbage Collection Management System

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

Predicting the moisture content of Daqu with hyperspectral imaging

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