Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environmen...Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environment,the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning.Furthermore,attention models,driven by human visual procedures,have become vital in natural hazard-related studies.Hence,this paper proposes an enhanced YOLOv5(You Only Look Once version 5)network for improved satellite-based landslide detection,embedded with two popular attention modules:CBAM(Convolutional Block Attention Module)and ECA(Efficient Channel Attention).These attention mechanisms are incorporated into the backbone and neck of the YOLOv5 architecture,distinctly,and evaluated across three YOLOv5 variants:nano(n),small(s),and medium(m).The experiments use opensource satellite images from three distinct regions with complex terrain.The standard metrics,including F-score,precision,recall,and mean average precision(mAP),are computed for quantitative assessment.The YOLOv5n+CBAM demonstrates the most optimal results with an F-score of 77.2%,confirming its effectiveness.The suggested attention-driven architecture augments detection accuracy,supporting post-landslide event assessment and recovery.展开更多
The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the...The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics,which presents significant hurdles in terms of channel estimation.In addition,the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation.To address the aforementioned issues,this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network.In this method,the time-frequency response of the channel is considered as a two-dimensional image.The Fast Super-Resolution Convolution Neural Network(FSRCNN)is used to first reconstruct channel images.Next,the Denoising Convolution Neural Network(DnCNN)is applied to reduce the channel noise and improve the accuracy of channel estimation.Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method,while also exhibiting reduced algorithmic complexity.展开更多
In many non-motor vehicle traffic accidents in China,the main cause of injury or death for drivers is not wearing a helmet.Therefore,the detection and punishment of such riders hold great significance in protecting pe...In many non-motor vehicle traffic accidents in China,the main cause of injury or death for drivers is not wearing a helmet.Therefore,the detection and punishment of such riders hold great significance in protecting people's lives and property safety.This paper delves into a deep learning-based method for detecting helmet-wearing on electric vehicles.The approach involves studying and designing an improved YOLOv5 model to identify the violation behavior of not wearing a helmet,including inserting the SE module in the network of the visual attention mechanism into the enhanced backbone network;bidirectional feature fusion is significantly enhanced by substituting the concat module with the Bidirectional Feature Pyramid Network(BiFPN)module,and adding receptive field attention Convolution(RFAConv)to the detection head.The improved YOLOv5 model demonstrates a higher mean Average Precision(mAP)while achieving a relatively smaller model size.This method provides technical support for the real-time and accurate detection of non-vehicle helmet targets;its efficacy has been confirmed through analysis of experimental results.These findings suggest that this method can assist traffic management departments in supervising non-motor vehicles,carrying significant practical value and importance.展开更多
引入了Inception-SE卷积模块组来提升LeNet-5网络的广度与深度,运用SE模块增强了有用的特征并抑制了对当前任务用处不大的特征;使用BN层和Dropout优化网络,防止梯度弥散,提升精度;使用全局池化层(global average pooling,GAP)代替全连...引入了Inception-SE卷积模块组来提升LeNet-5网络的广度与深度,运用SE模块增强了有用的特征并抑制了对当前任务用处不大的特征;使用BN层和Dropout优化网络,防止梯度弥散,提升精度;使用全局池化层(global average pooling,GAP)代替全连接层来减少网络计算参数.研究结果表明:改进后网络的识别精度达到了99.88%,比传统的LeNet-5网络提高了1.71%.展开更多
基金supported by the Department of Science and Technology,Science and Engineering Research Board,New Delhi,India,under Grant No.EEQ/2022/000812.
文摘Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environment,the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning.Furthermore,attention models,driven by human visual procedures,have become vital in natural hazard-related studies.Hence,this paper proposes an enhanced YOLOv5(You Only Look Once version 5)network for improved satellite-based landslide detection,embedded with two popular attention modules:CBAM(Convolutional Block Attention Module)and ECA(Efficient Channel Attention).These attention mechanisms are incorporated into the backbone and neck of the YOLOv5 architecture,distinctly,and evaluated across three YOLOv5 variants:nano(n),small(s),and medium(m).The experiments use opensource satellite images from three distinct regions with complex terrain.The standard metrics,including F-score,precision,recall,and mean average precision(mAP),are computed for quantitative assessment.The YOLOv5n+CBAM demonstrates the most optimal results with an F-score of 77.2%,confirming its effectiveness.The suggested attention-driven architecture augments detection accuracy,supporting post-landslide event assessment and recovery.
基金funded in part by the National Natural Science Foundation of China(62261024 and U2001213)in part by National Key Research and Development Project(2020YFB1807204)+2 种基金in part by Science and Technology Project of Education Department of Jiangxi Province(GJJ214606 and GJJ2205201)in part by Key Laboratory of Universal Wireless Communications(BUPT),Ministry of Education,P.R.China(KFKT-2022101)in part by the Jiangxi Provincial Natural Science Foundation(20212BAB212001)。
文摘The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics,which presents significant hurdles in terms of channel estimation.In addition,the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation.To address the aforementioned issues,this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network.In this method,the time-frequency response of the channel is considered as a two-dimensional image.The Fast Super-Resolution Convolution Neural Network(FSRCNN)is used to first reconstruct channel images.Next,the Denoising Convolution Neural Network(DnCNN)is applied to reduce the channel noise and improve the accuracy of channel estimation.Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method,while also exhibiting reduced algorithmic complexity.
文摘In many non-motor vehicle traffic accidents in China,the main cause of injury or death for drivers is not wearing a helmet.Therefore,the detection and punishment of such riders hold great significance in protecting people's lives and property safety.This paper delves into a deep learning-based method for detecting helmet-wearing on electric vehicles.The approach involves studying and designing an improved YOLOv5 model to identify the violation behavior of not wearing a helmet,including inserting the SE module in the network of the visual attention mechanism into the enhanced backbone network;bidirectional feature fusion is significantly enhanced by substituting the concat module with the Bidirectional Feature Pyramid Network(BiFPN)module,and adding receptive field attention Convolution(RFAConv)to the detection head.The improved YOLOv5 model demonstrates a higher mean Average Precision(mAP)while achieving a relatively smaller model size.This method provides technical support for the real-time and accurate detection of non-vehicle helmet targets;its efficacy has been confirmed through analysis of experimental results.These findings suggest that this method can assist traffic management departments in supervising non-motor vehicles,carrying significant practical value and importance.
文摘引入了Inception-SE卷积模块组来提升LeNet-5网络的广度与深度,运用SE模块增强了有用的特征并抑制了对当前任务用处不大的特征;使用BN层和Dropout优化网络,防止梯度弥散,提升精度;使用全局池化层(global average pooling,GAP)代替全连接层来减少网络计算参数.研究结果表明:改进后网络的识别精度达到了99.88%,比传统的LeNet-5网络提高了1.71%.
