Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral...Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.展开更多
Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,w...Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.展开更多
The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,w...The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,which is important for object retrieval and treatment of the patient.This case report describes a male child,2 years and 2 mo of age,who accidentally swallowed a lithium battery while playing at home.A plain X-ray showed that the battery was in the abdomen,but it was unclear whether the object was still inside the stomach.A second X-ray examination performed after oral administration of a bloating agent to produce expansion of the stomach and provide negative contrast confirmed that the ingested battery was still in the stomach.The battery was then carefully removed using magnetic and balloon catheters under fluoroscopic guidance.This case report describes the successful use of an orally administered bloating agent without pain to the child in orderto determine the precise location of a foreign object in the abdomen.展开更多
With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WP...With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WPT)because of its high reliability,safety,and convenience,especially high suitability for the future self-driving scenario.Foreign object detection(FOD),mainly including metal object detection and living object detection,is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety.In the last decade,especially in the past three years,many pieces of research on FOD have been reported.This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity,reliability,adaptability,complexity,and cost.Future challenges for research and development are also discussed to encourage commercialisation of EV-WPT technique.展开更多
Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult...Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.展开更多
针对输电线路上附着异物容易对周围环境造成污染甚至导致短路停电及发生火灾等问题,提出了一种基于改进YOLOv8n的输电线路及铁塔异物实时检测算法,采用的基线算法为YOLOv8n,为了提高对小目标的检测准确率并且提升对复杂背景下异物的检...针对输电线路上附着异物容易对周围环境造成污染甚至导致短路停电及发生火灾等问题,提出了一种基于改进YOLOv8n的输电线路及铁塔异物实时检测算法,采用的基线算法为YOLOv8n,为了提高对小目标的检测准确率并且提升对复杂背景下异物的检测能力,将原始YOLOv8n算法的检测头改进为动态检测头DynamicHead,提高了模型对多个维度特征的提取能力与应对不同输入的动态调整能力,将非极大值抑制(Non-Maximum Suppression,NMS)算法改进为Soft-NMS,提高了模型的泛化能力和整体检测性能。试验结果表明,改进后的算法检测平均精度均值(mean Average Precision,mAP)为95.7%,相比于原YOLOv8n算法提升了4.4个百分点,在保证可满足实时检测速度的同时实现了较高的检测精度,具有较好的有效性和实用性。展开更多
针对铁路轨道异物侵限检测精度偏低、速度偏慢、易出现漏检与误检的问题,提出一种基于浅层特征融合的轻量级铁轨异物侵限检测算法(YOLO Lightweight and Shallow-feature Fusion,YOLO-LSF).首先,结合YOLOv8n特征提取网络,基于GhostConv...针对铁路轨道异物侵限检测精度偏低、速度偏慢、易出现漏检与误检的问题,提出一种基于浅层特征融合的轻量级铁轨异物侵限检测算法(YOLO Lightweight and Shallow-feature Fusion,YOLO-LSF).首先,结合YOLOv8n特征提取网络,基于GhostConv改进C2f模块以构建C2f_Ghost模块,从而降低模型的参数量和计算量;其次,在骨干网络尾端引入MLCA注意力机制,增强目标区域的特征信息,优化模型的特征提取效率;再次,利用可变形卷积DCNv2替换YO-LOv8n中C2f模块的部分普通卷积,构建了C2f_DCNv2模块,增强模型的特征提取能力;最后,在颈部网络中融入主干网络中的浅层特征信息,较好地解决了经多次卷积操作所导致的细节信息丢失问题,以提升模型对远距离异物(小目标)的检测能力.实验结果表明:在自建的铁轨异物入侵检测数据集上,相比于原YOLOv8n算法,采用YOLO-LSF算法处理的平均精度提升了5.2%,每秒帧数(Frames Per Second,FPS)提升了3.37%,参数量减少了20.1%,计算量减少了22.2%,有效提升了复杂环境下铁轨异物目标的检测精度与检测速度,降低了漏检与误检的概率.展开更多
文摘Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.
基金supported by a grant from the National Key Research and Development Project(2023YFB4302100)Key Research and Development Project of Jiangxi Province(No.20232ACE01011)Independent Deployment Project of Ganjiang Innovation Research Institute,Chinese Academy of Sciences(E255J001).
文摘Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.
文摘The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,which is important for object retrieval and treatment of the patient.This case report describes a male child,2 years and 2 mo of age,who accidentally swallowed a lithium battery while playing at home.A plain X-ray showed that the battery was in the abdomen,but it was unclear whether the object was still inside the stomach.A second X-ray examination performed after oral administration of a bloating agent to produce expansion of the stomach and provide negative contrast confirmed that the ingested battery was still in the stomach.The battery was then carefully removed using magnetic and balloon catheters under fluoroscopic guidance.This case report describes the successful use of an orally administered bloating agent without pain to the child in orderto determine the precise location of a foreign object in the abdomen.
基金Key R&D Program of Guangdong Province,China(No.2020B0404030004)partly by the open research fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)(No.GML-KF-22-19)partly by the National Natural Science Foundation of China(No.62001301).
文摘With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WPT)because of its high reliability,safety,and convenience,especially high suitability for the future self-driving scenario.Foreign object detection(FOD),mainly including metal object detection and living object detection,is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety.In the last decade,especially in the past three years,many pieces of research on FOD have been reported.This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity,reliability,adaptability,complexity,and cost.Future challenges for research and development are also discussed to encourage commercialisation of EV-WPT technique.
基金supported by the Laboratory of Lingnan Modern Agriculture Project(Grant No.NT2021009)Guangdong University Key Field(Artificial Intelligence)Special Project(No.2019KZDZX1012)and the 111 Project(D18019)+3 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515110554)China Postdoctoral Science Foundation(Grant No.2022M721201)the National Natural Science Foundation of China(Grant No.31901411)The Open Competition Program of the Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province(No.2022SDZG03).
文摘Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.
文摘针对输电线路上附着异物容易对周围环境造成污染甚至导致短路停电及发生火灾等问题,提出了一种基于改进YOLOv8n的输电线路及铁塔异物实时检测算法,采用的基线算法为YOLOv8n,为了提高对小目标的检测准确率并且提升对复杂背景下异物的检测能力,将原始YOLOv8n算法的检测头改进为动态检测头DynamicHead,提高了模型对多个维度特征的提取能力与应对不同输入的动态调整能力,将非极大值抑制(Non-Maximum Suppression,NMS)算法改进为Soft-NMS,提高了模型的泛化能力和整体检测性能。试验结果表明,改进后的算法检测平均精度均值(mean Average Precision,mAP)为95.7%,相比于原YOLOv8n算法提升了4.4个百分点,在保证可满足实时检测速度的同时实现了较高的检测精度,具有较好的有效性和实用性。
文摘针对铁路轨道异物侵限检测精度偏低、速度偏慢、易出现漏检与误检的问题,提出一种基于浅层特征融合的轻量级铁轨异物侵限检测算法(YOLO Lightweight and Shallow-feature Fusion,YOLO-LSF).首先,结合YOLOv8n特征提取网络,基于GhostConv改进C2f模块以构建C2f_Ghost模块,从而降低模型的参数量和计算量;其次,在骨干网络尾端引入MLCA注意力机制,增强目标区域的特征信息,优化模型的特征提取效率;再次,利用可变形卷积DCNv2替换YO-LOv8n中C2f模块的部分普通卷积,构建了C2f_DCNv2模块,增强模型的特征提取能力;最后,在颈部网络中融入主干网络中的浅层特征信息,较好地解决了经多次卷积操作所导致的细节信息丢失问题,以提升模型对远距离异物(小目标)的检测能力.实验结果表明:在自建的铁轨异物入侵检测数据集上,相比于原YOLOv8n算法,采用YOLO-LSF算法处理的平均精度提升了5.2%,每秒帧数(Frames Per Second,FPS)提升了3.37%,参数量减少了20.1%,计算量减少了22.2%,有效提升了复杂环境下铁轨异物目标的检测精度与检测速度,降低了漏检与误检的概率.
