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Household Garbage Classification: A Transfer Learning Based Method and a Benchmark 1

11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Cheng, Qian Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Li, Huan-Yi Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui23060 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

Household garbage images are highly diverse in color, texture and geometry, which poses significant challenges to garbage classification. Deep convolutional neural network (DCNN) have recently achieved remarkable progress due to their ability to learn high-level feature representations. It usually requires a large number of labelled image data for training a DCNN model. However, there are few public and mature data sets concerned on household garbage images. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for household garbage image classification. This data set is called 30 Types of Household Garbage Images (HGI-30), which contains 6′000 images of 30 household garbage types, with complex backgrounds, different resolutions, and complicated variations in sample, pose, illumination and background. The publicly available HGI-30 data set allows researchers to develop more accurate and robust methods for both household garbage image processing and interpretation analysis of household garbage object. We further study the classification problem on this data set and propose a transfer learning based method, also provide a performance analysis, which serves as baseline result on this benchmark. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Lightweight Neural Network Based Garbage Image Classification Using a Deep Mutual Learning 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Liu, Xiao Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Xu, Li-Xiang Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui230601 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

With the construction and development of civilized cities, image based garbage classification has gradually become an important concern in computer vision community. During the algorithms for image classification, the strong ability of Convolution Neural Networks (CNNs) in feature learning makes it the most successful approach at the moment. However, the parameters of CNNs model are very huge, and its training usually depends on a large amount of samples. In this article, we tackle the problem of lightweight neural network based garbage image classification, which aims to learn classifier with a small number of model parameters. Specifically, we utilize the MobileNetV2 for the backbone of feature extraction network and jointly train such two nets in a way of deep mutual learning. It realizes the information distillation between the teacher and the student. With this, we can significantly improve the learning ability of the MobileNetV2 based lightweight neural network. The experimental results on a self-assembled dataset show that our proposal effectively classifies the garbage and achieves a classification effect batter than the state of the arts in terms of testing accuracy, time and model size. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Industrial Smoke Image Segmentation Based on a New Algorithm of Cross-Entropy Model 16th

Industrial Smoke Image Segmentation Based on a New Algorithm...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Huang, Qian-Jing Zou, Le Wu, Zhi-Ze Li, Huan-Yi Wang, Xiao-Feng Chen, Yan-Ping Department of Environmental Engineering Hefei University HefeiAnhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University HefeiAnhui230601 China

ISBN: (纸本)9783030607982

Smoke segmentation from the industrial images is a key concern of environmental monitoring. As the similarities between the gray value of the background and the smoke, the existing segmentation algorithms are difficult to accurately segment the target smoke. In this paper, we construct a cross-entropy based industrial smoke image segmentation by integrating the iterative convolution-thresholding. Specially, we use the iterative convolution-thresholding to implicitly represent the interface of each image domain through a characteristic function. We further perform the combination of a regularization term and a fidelity term in the cross-entropy model. In the proposed algorithm, the fidelity term is first converted into the product of the characteristic function and the cross-entropy function. Then the functional of the characteristic function is used to obtain the regularization term by the approach of thermonuclear convolution approximation. The experimental results demonstrate that our proposal has a more accurate segmentation effect and higher segmentation efficiency. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Influence of roll-bending deformation on microstructure and texture in an Al-Li alloy with different extrusion aspect ratio 8

Influence of roll-bending deformation on microstructure and ...

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8th International Conference on Recrystallization and Grain Growth, Rex and GG 2023

作者： Hou, X.K. Qi, Y.T. Li, B.L. Yin, J.M. Qi, P. Wen, S.P. Wei, W. Yao, C.G. Hu, Z.G. Nie, Z.R. Faculty of Materials and Manufacturing National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China Aerospace Research Institute of Materials & Processing Technology Beijing100076 China Beijing Institute of Astronautical Systems Engineering Beijing100076 China

The purpose of this investigation is to study the effect of roll bending deformation on the microstructure and texture in an Al-Li alloy with different extrusion aspect ratios. The results indicated that the substructure of most extruded fiber grains is not completely eliminated on the ND-ED section at position B with a low extrusion aspect ratio. However, the texture type of the ND-TD section changes from a single texture to an adjacent texture after roll-bending deformation, and the texture intensity decreases from 8.3 to 6.1. With the increase of the extrusion aspect ratio, the effect of roll-bending deformation on the microstructure began to be obvious, especially at position C. A large number of elongated recrystallized grains along the ED direction were transformed into approximately equiaxed grains on ND-ED section after the roll-bending deformation. There were also a small number of approximately equiaxed cell structures. The relative frequency of the low-angle grain boundary rose slightly from 42.1% to 46.7%. The single texture moved to the adjacent texture, and the texture strength increased from 4.8 to 6.3. © Published under licence by IOP Publishing Ltd.

关键词： Textures

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Research on Bilateral Co-construction of Transportation Informationization Supply and Demand Based on big data Analysis

Research on Bilateral Co-construction of Transportation Info...

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2020 International Conference on Building technology and Computer Science, BTCS 2020

作者： Xie, Zilong Gansu Province Transportation Planning Survey and Design Institute Co. Ltd 730000 China Gansu Prov. Engineering Research Center of Integrated Transportation Big Data Application Technology 730000 China

With the development of science and technology in our country, network information technology has been continuously integrated into transportation. Chinese traffic informatization has achieved great results, which provides better traffic services for people. However, compared with developed countries, Chinese transportation informatization still has many problems to be solved. Based on this, this article uses computer big data technology to first analyze the significance of traffic management informationization. Then, this paper analyses the unbalance of supply-demand relationship and its influencing factors. Finally, some suggestions are put forward. © Published under licence by IOP Publishing Ltd.

关键词： Informatization

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Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images

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Acta Oceanologica Sinica 2022年第7期41卷 166-179页

作者： Kai Du Yi Ma Zongchen Jiang Xiaoqing Lu Junfang Yang College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao266590China First Institute of Oceanology Ministry of Natural ResourcesQingdao266061China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin150001China National Satellite Ocean Application Service Beijing100081China College of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao266580China Technology Innovation Center for Ocean Telemetry Ministry of Natural ResourcesQingdao266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072China

Accurate detection of an oil spill is of great significance for rapid response to oil spill *** images have the advantages of high spatial resolution,short revisit period,and wide imaging width,which is suitable for large-scale oil spill ***,in wide remote sensing images,the number of oil spill samples is generally far less than that of seawater ***,the sea surface state tends to be heterogeneous over a large area,which makes the identification of oil spills more difficult because of various sea conditions and *** address this problem,we used the F-Score as a measure of the distance between forecast value and true value,proposed the Class-Balanced F loss function(CBF loss function)that comprehensively considers the precision and recall,and rebalances the loss according to the actual sample numbers of various *** the CBF loss function,we constructed convolution neural networks(CBF-CNN)for oil spill *** on the image acquired by the Coastal Zone Imager(CZI)of the Haiyang-1C(HY-1C)satellite in the Andaman Sea(study area 1),we carried out parameter adjustment *** contrast to experiments of different loss functions,the F1-Score of the detection result of oil emulsions is 0.87,which is 0.03–0.07 higher than cross-entropy,hinge,and focal loss functions,and the F1-Score of the detection result of oil slicks is 0.94,which is 0.01–0.09 higher than those three loss *** comparison with the experiment of different methods,the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks,supports vector machine and random forests models,and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three *** verify the applicability of the CBF-CNN model in different observation scenes,we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments,which include two stu

关键词： oil spills CNN classification loss function sunglint detection

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Identifying stepping-stones within small wetlands in highly urbanized areas and under network construction: A case study of the Guangdong–Hong Kong–Macao greater Bay Area, China

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Ecological Indicators 2025年 176卷

作者： Ying Ye Caixia Chen Chunxue Yu Caige Sun Shoubao Geng Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Province Engineering Laboratory for Geographic Spatio-Temporal Big Data Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 China School of Geography South China Normal University Guangzhou 510631 China Research Center for Eco-environmental Engineering Dongguan University of Technology Dongguan 523808 China

In highly urbanized areas, small wetlands can provide multiple ecosystem services while benefiting the well-being of citizens. Although existing studies on wetland ecological networks have prioritized large wetland conservation measures, insufficient attention has been paid to the distribution patterns of small wetlands and their role in ecological networks. Utilizing the Guangdong–Hong Kong–Macao Greater Bay Area as a case study, we constructed a wetland ecological network to identify ecological pinch points while identifying small wetlands suitable to function as stepping-stones. This approach aims to establish potential corridors within the network and to assess the connectivity and robustness of the network under various scenarios. Our findings identified 297 ecological pinch points within the wetland ecological network alongside 53 small wetlands designated as stepping-stones, predominantly located in Foshan and Zhongshan cities in the central and western regions of the study area. The stepping-stone network comprised of 241 ecological corridors, with enhancements observed in the α, β, and γ indices, thereby significantly expanding the network’s connectivity. Robustness index alterations in the stepping-stone network were moderated in relation to the ecological network, demonstrating that small wetlands can effectively bolster ecological network stability. It is recommended that, considering the distribution of ecological stepping stones of small wetlands, priority be given to the conservation and restoration of small wetlands in Foshan and Zhongshan within the central and western regions of the Guangdong–Hong Kong–Macao Greater Bay Area. This includes delineating conservation zones for small wetlands or establishing clusters of wetland parks centered on small wetlands, thereby maximizing their ecosystem value in supporting the urban agglomeration of the Guangdong–Hong Kong–Macao Greater Bay Area.

关键词： Small wetland Stepping-stone Wetland ecological network Guangdong-Hong Kong-Macao Greater Bay Area

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Atmospheric correction for nighttime light image using radiative transfer model

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Hongqing Ren, Huazhong Li, Fengguang Lin, Songyi Ye, Hanlin Jiang, Chenchen Zhu, Jinshun Wang, Baozhen Peking University Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Beijing 100871 China Peking University Beijing Key Lab of Spatial Information Integration and Its Application Beijing 100871 China International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China CAS Institute of Atmospheric Physics Beijing China Shanghai Ocean University College of Information Technology Shanghai 201306 China

Nighttime light (NTL) remote sensing data has been widely used in various fields, such as human activity analysis, urbanization studies, and economic evaluation. However, Earth’s nighttime environment is very complex so that the NTL images are seriously affected by atmospheric effect and moonlight. This complexity primarily stems from the numerous atmospheric scattering and absorption, as well as the incoming moonlight, which can significantly distort and contaminate nighttime light observed by the satellite and consequently reduce the precision and stability of NTL data. In order to improve the quantitatively quality of the NTL data, this paper proposes an innovative atmospheric correction algorithm that leverages the nighttime radiative transfer model (nRTM) considering both atmospheric effect and moonlight effect to get ground radiance of artificial lights from satellite nighttime light images. This model takes into account the complex interactions between light and the atmosphere. By simulating these processes, the algorithm is able to separate the contributions of atmospheric scattering and absorption from the original NTL images. To demonstrate the effectiveness of the proposed algorithm, this paper takes the SDGSAT-1 NTL image of Beijing as a representative case study of atmospheric correction. By comparing the corrected and uncorrected images, it is evident that the atmospheric correction significantly improves the quality of the NTL data and the ground nighttime lighting information becomes clearer and more accurate, effectively removing noise interference and enhancing data reliability. Moreover, it also found that the high-pressure sodium (HPS) lamps and LED lamps in the NTL images presented different radiance values and spectral shapes that can be helpful for classifying different lamps. © 1980-2012 IEEE.

关键词： atmospheric correction NTL RTM SDGSAT-1

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Flooded Cropland Mapping Based on GF-3 and Mapbox Imagery Using Semantic Segmentation: A Case Study of Typhoon Siamba in Western Guangdong in July 2022

Flooded Cropland Mapping Based on GF-3 and Mapbox Imagery Us...

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2022 International Conference on Geographic Information and Remote Sensing technology, GIRST 2022

作者： Ku, Mengjun Jiang, Hao Li, Dan Wang, Chongyang Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781510662186

Typhoon Siamba made landfall in Western Guangdong on July 2, 2022, causing great losses to crops in western Guangdong. Radar remote sensing can penetrate through clouds and fog, is suitable for identifying flooded areas before or after typhoons and in rainy weather. However, radar flooded waterbody mapping faces two major problems: distinguishing between flooded areas and natural waterbody, and the other is noise interference from confusing ground objects. Aiming at these problems, the study proposes a method of high-precision cropland information combined with waterbody identification. Based on GF-3 and Mapbox data, this paper first uses watershed semantic segmentation to extract initial waterbody, then uses SegFormer deep learning technology to identify cropland, finally realizes flooded cropland mapping based on cropland information. This study concluded that the affected cropland in Zhanjiang and Maoming City, Guangdong Province, China is 75.437 km2 and 31.175 km2 respectively. The cropland extraction accuracy and Intersection over Union (IoU) are 96.65% and 92.64% respectively. The study shows that flood monitoring combined with cropland identification information can effectively avoid noise interference and accurately extract the flood range, and achieve high-precision flooded cropland mapping. © 2023 SPIE.

关键词： Watersheds

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Long-Term and Short-Term Perception in Knowledge Tracing 1

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11th CCF big data Conference, bigdata 2023

作者： Chen, Zihang Zhu, Mengxiao Wang, Fei Shen, Shuanghong Huang, Zhenya Liu, Qi Anhui Province Key Laboratory of Science Education and Communication Institute of Advanced Technology and School of Humanities and Social Sciences University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9789819989799

ISBN: (纸本)9789819989782

Knowledge Tracing (KT) is a fundamental task in contemporary intelligent educational systems, which tracks the knowledge states of the learners based on their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramatically compared with the traditional probabilistic approaches. However, deep learning based KT models also have significant limitations. For example, Recurrent Neural Network (RNN)-based KT models can not capture dependencies in long-term sequences effectively, and attention mechanism-based KT models rely on the positional encoding to perceive sequential information, which may disrupt the semantics of the original embeddings. Moreover, the data sparsity question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called "Long-term and Short-term perception in knowledge tracing (LSKT)", which uses multilayer perceptrons and attention mechanism to capture the long-term and short-term dependencies, and employs 2PL-IRT based embedding to alleviate the data sparsity question. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed LSKT model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Deep Learning Educational data Mining Intelligent Education Knowledge Tracing

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