Cotton fibres, picking, ginning, spinning and weaving

Negm, M. A.; Sanad, S.H.

doi:10.1016/b978-0-12-818782-1.00001-8

Cited by 16 publications

(12 citation statements)

References 6 publications

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“…The fibers go through ginning, opening, mixing, carding, drawing, doubling, and spinning. 27 Operations taking place in the blowroom include carding, drawing, combing, and use of speed frame, ring frame, and winding machines. 28,29 The electricity used to run these machines leads to CO 2 emissions.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Carbon Footprint of Various Cotton Knitwear Production Processes in Bangladesh

Amin

Mahmud

Anannya

2021

AATCC Journal of Research

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Carbon footprint is defined as the amount of greenhouse gases generated during the whole lifetime or within a specific boundary of a product. This study measured the average carbon footprint of some common cotton knitwear T-shirts, polo shirts, and fleece jackets during production using CCalC2 software. Energy and water consumption data were taken as the study's input, while direct and indirect emissions were not specified. The results show the amount of carbon dioxide emitted at different stages of knitwear production. Cotton processing and yarn spinning resulted in relatively high carbon dioxide emissions, while wet treatment gave similar results. Fleece jacket production gave high amounts of emitted carbon dioxide due to its complex fabric structure and high fabric weight, while T-shirts gave the lowest amount of the fabrics tested.

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Section: Resultsmentioning

confidence: 99%

Assessment of Carbon Footprint of Various Cotton Knitwear Production Processes in Bangladesh

Amin

Mahmud

Anannya

2021

AATCC Journal of Research

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“…Cotton occupies an important position in the national economy as an important cash crop and essential resource for livelihoods [1]. In the picking, storage, transport and processing of raw cotton it is susceptible to a variety of foreign contamination, if not screened and removed in a timely manner, it is easy to cause textile yarn breakage, blemishes and uneven colouring.…”

Section: Introductionmentioning

confidence: 99%

A lightweight network of near cotton‐coloured impurity detection method in raw cotton based on weighted feature fusion

et al. 2023

IET Image Processing

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The objective is to improve the recognition rate of white and near cotton-coloured impurities in raw cotton against a single white visible light source background. A lightweight detection network model without anchor boxes based on improved YOLO v4-tiny is proposed in this paper based on weighted feature fusion (WFF). The WFF strategy was used to improve the detection accuracy of the improved YOLO v4-tiny algorithm. Meanwhile, to address the disadvantage that the anchor boxes obtained by the K-means algorithm clustering do not have global features, the anchor-free and tiny decoupled head schemes are used instead of the traditional coupled detection head. The improved algorithm was validated on the PASCAL VOC2012 dataset and the self-built raw cotton impurity dataset collected by high-speed camera. On the raw cotton impurity test set, compared to the original YOLO v4-tiny model, the mean average precision (mAP) and frames per second (FPS) of the improved model increased by 10.35% and 6.9%, respectively. The proposed model detects white and near cotton-coloured impurities with an accuracy of up to 98.78% and 98.00%. The experimental results show that the proposed method can effectively detect and identify impurities in raw cotton, which is of great practical significance for foreign matter detection and identification in cotton.

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“…It is widely used in industrial production and daily life because of its wide planting range, large yield, and low production cost. Being affected by varieties, planting environment, climate, weather, and other factors, the physical and chemical properties of cotton fibers will be different, which directly affects the quality of cotton and cotton products [1][2][3][4]. Evaluating the quality of cotton objectively and accurately is of great significance for making full use of cotton with different quality, designing optimal production processes, and improving the quality of cotton products.…”

Section: Introductionmentioning

confidence: 99%

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

He¹,

Wang²

2021

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Currently, analyzing the microscopic image of cotton fiber cross-section is the most accurate and effective way to measure its grade of maturity and then evaluate the quality of cotton samples. However, existing methods cannot extract the edge of the cross-section intact, which will affect the measurement accuracy of maturity grade. In this paper, a new edge detection algorithm that is based on the RCF convolutional neural network (CNN) is proposed. For the microscopic image dataset of the cotton fiber cross-section constructed in this paper, the original RCF was firstly used to extract the edge of the cotton fiber cross-section in the image. After analyzing the output images of RCF in each convolution stage, the following two conclusions are drawn: (1) the shallow layers contain a lot of important edge information of the cotton fiber cross-section; (2) because the size of the cotton fiber cross-section in the image is relatively small and the receptive field of the convolutional layer gradually increases with the deepening of the number of layers, the edge information detected by the deeper layers becomes increasingly coarse. In view of the above two points, the following improvements are proposed in this paper: (1) modify the network supervision model and loss calculation structure; (2) the dilated convolution in the deeper layers is removed; therefore, the receptive field in the deeper layers is reduced to adapt to the detection of small objects. The experimental results show that the proposed method can effectively improve the accuracy of edge extraction of cotton fiber cross-section.

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Cotton fibres, picking, ginning, spinning and weaving

Cited by 16 publications

References 6 publications

Assessment of Carbon Footprint of Various Cotton Knitwear Production Processes in Bangladesh

Assessment of Carbon Footprint of Various Cotton Knitwear Production Processes in Bangladesh

A lightweight network of near cotton‐coloured impurity detection method in raw cotton based on weighted feature fusion

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

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