Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the preva...Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.展开更多
Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images....Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.展开更多
In order to study the characters of chemical kinetics for organophosphorus migration in clay with different pH, waste of organophosphorus was put under pressure to leakage permeating the cohesive soil, and simulate th...In order to study the characters of chemical kinetics for organophosphorus migration in clay with different pH, waste of organophosphorus was put under pressure to leakage permeating the cohesive soil, and simulate the process of organophosphorus leakage permeating the Aquitard, searching the characters of chemical kinetics for organophosphorus migration in clay with different pH. It is shown that the ability of migration of organophosphorus leakage permeating the cohesive soil fall with increase of pH; the penetration rate of organophosphorus is about 1.25% when pH is 7.5, organophosphorus has not penetrated the cohesive soil when pH is equal or greater than 8.5. The effect of retardarce is obvious. Concentration of PO43？ that comes from the mineralization of organophosphorus is lowered slightly with increases of pH of clay, and rise with extension of time. The Chemical Kinetics equation is log c=-0.1pH＋0.2172k1t＋S.展开更多
基金funded in part by theHenan ProvinceKeyR&DProgramProject,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”under Grant No.231111210400.
文摘Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.
基金supported by the School Doctoral Fund of Zhengzhou University of Light Industry No.2015BSJJ051.
文摘Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.
基金supported by the Knowledge Innovation Programs of Chinese Academy of Sciences(No.KZCX2YWQ062,KZCX2YW126)
文摘In order to study the characters of chemical kinetics for organophosphorus migration in clay with different pH, waste of organophosphorus was put under pressure to leakage permeating the cohesive soil, and simulate the process of organophosphorus leakage permeating the Aquitard, searching the characters of chemical kinetics for organophosphorus migration in clay with different pH. It is shown that the ability of migration of organophosphorus leakage permeating the cohesive soil fall with increase of pH; the penetration rate of organophosphorus is about 1.25% when pH is 7.5, organophosphorus has not penetrated the cohesive soil when pH is equal or greater than 8.5. The effect of retardarce is obvious. Concentration of PO43？ that comes from the mineralization of organophosphorus is lowered slightly with increases of pH of clay, and rise with extension of time. The Chemical Kinetics equation is log c=-0.1pH＋0.2172k1t＋S.
