The rapid expansion of photovoltaic(PV)power plants has created a pressing need for efficient and reliable operation and maintenance(O&M).Traditional manual inspection is slow,costly,and prone to error,motivating ...The rapid expansion of photovoltaic(PV)power plants has created a pressing need for efficient and reliable operation and maintenance(O&M).Traditional manual inspection is slow,costly,and prone to error,motivating the use of unmanned aerial vehicles(UAVs)with infrared and visible cameras for automated monitoring.In this paper,we propose a YOLO-based multi-task framework for simultaneous PV defect detection and hazard-level classification.We constructed a dataset of 5,000 annotated UAV images from the Riyue PV power plant,covering ten defect categories and four severity levels(LV1-LV4).To support severity grading,the YOLO architecture was extended with a dual-task head and an ordinal regression scheme.The model was trained with a compound loss combining bounding-box regression,objectness,defect classification,and hazard-level supervision.Experimental evaluation on real UAV inspection data(224 strings,30 ground-truth defects)shows that the proposed approach achieves mAP50 of 95.6%,recall of 92.7%,and severity classification accuracy of 90.8%.The system detects both minor anomalies(e.g.,bird droppings,soiling)and critical faults(e.g.,missing panels,disconnections)in real time at over 40 FPS,providing actionable insights for maintenance prioritization.These results demonstrate that YOLO-based UAV inspection offers a robust and scalable solution for intelligent PV O&M.展开更多
The rapid expansion of photovoltaic(PV)deployment poses new challenges for large-scale and distributed maintenance,particularly in fishery-PV complementary plants where panels are deployed over water surfaces.This pap...The rapid expansion of photovoltaic(PV)deployment poses new challenges for large-scale and distributed maintenance,particularly in fishery-PV complementary plants where panels are deployed over water surfaces.This paper presents the design and implementation of an intelligent operation and maintenance(O&M)system that integrates a 3D holographic digital twin cloud platform with UAV-assisted inspection and localized cleaning.The proposed system supports multi-source data acquisition,including UAV imagery,infrared sensing,and DustIQ-based soiling monitoring,and provides real-time visualization of the PV plant through 1:13D reconstruction.UAVs are employed for both autonomous inspections,covering defects such as soiling,bird droppings,bypass diode faults,and panel disconnections and targeted cleaning in small water-covered areas.Field trials were conducted at Riyue and Chebu PV plants,with small-scale UAV cleaning validation in Chebu fish ponds.Results demonstrated that the system achieves efficient task scheduling,fault detection,and localized cleaning,thereby improving O&M efficiency,reducing costs,and enabling digitalized and intelligent management for large-scale PV stations.展开更多
The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection an...The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging(LIDAR)and HD maps with high level annotations.In this paper,we propose a scalable and affordable data collection and annotation framework image-to-map annotation proximity(I2MAP),for affordance learning in autonomous driving applications.We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map(OSM).Our benchmark consists of 40000 images with more than40 affordance labels under various day time and weather even with very challenging heavy snow.We implemented sample advanced driver-assistance systems(ADAS)functions by training our data with neural networks(NN)and cross-validate the results on benchmarks like KITTI and BDD100K,which indicate the effectiveness of our framework and training models.展开更多
Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the...Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.展开更多
The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are...The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread use.Over-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.展开更多
Virtual simulation testing of Autonomous Vehicles(AVs)is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs.Mainstream methods focus on improving testing efficiency by ex...Virtual simulation testing of Autonomous Vehicles(AVs)is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs.Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets.However,the criticalities defined in their testing tasks are based on fixed assumptions,the obtained scenarios cannot pose a challenge to AVs with different strategies.To fill this gap,we propose an intelligent testing method based on operable testing tasks.We found that the driving behavior of Surrounding Vehicles(SVs)has a critical impact on AV,which can be used to adjust the testing task difficulty to find more challenging scenarios.To model different driving behaviors,we utilize behavioral utility functions with binary driving strategies.Further,we construct a vehicle interaction model,based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty.Finally,by adjusting SV’s strategies,we can generate more corner cases when testing different AVs in a finite number of simulations.展开更多
基金supported in part by the Joint Innovation Program of Guangdong under Grant 2023A0505020003.
文摘The rapid expansion of photovoltaic(PV)power plants has created a pressing need for efficient and reliable operation and maintenance(O&M).Traditional manual inspection is slow,costly,and prone to error,motivating the use of unmanned aerial vehicles(UAVs)with infrared and visible cameras for automated monitoring.In this paper,we propose a YOLO-based multi-task framework for simultaneous PV defect detection and hazard-level classification.We constructed a dataset of 5,000 annotated UAV images from the Riyue PV power plant,covering ten defect categories and four severity levels(LV1-LV4).To support severity grading,the YOLO architecture was extended with a dual-task head and an ordinal regression scheme.The model was trained with a compound loss combining bounding-box regression,objectness,defect classification,and hazard-level supervision.Experimental evaluation on real UAV inspection data(224 strings,30 ground-truth defects)shows that the proposed approach achieves mAP50 of 95.6%,recall of 92.7%,and severity classification accuracy of 90.8%.The system detects both minor anomalies(e.g.,bird droppings,soiling)and critical faults(e.g.,missing panels,disconnections)in real time at over 40 FPS,providing actionable insights for maintenance prioritization.These results demonstrate that YOLO-based UAV inspection offers a robust and scalable solution for intelligent PV O&M.
基金Joint Innovation Program of Guangdong(Project No.:2023A0505020003)。
文摘The rapid expansion of photovoltaic(PV)deployment poses new challenges for large-scale and distributed maintenance,particularly in fishery-PV complementary plants where panels are deployed over water surfaces.This paper presents the design and implementation of an intelligent operation and maintenance(O&M)system that integrates a 3D holographic digital twin cloud platform with UAV-assisted inspection and localized cleaning.The proposed system supports multi-source data acquisition,including UAV imagery,infrared sensing,and DustIQ-based soiling monitoring,and provides real-time visualization of the PV plant through 1:13D reconstruction.UAVs are employed for both autonomous inspections,covering defects such as soiling,bird droppings,bypass diode faults,and panel disconnections and targeted cleaning in small water-covered areas.Field trials were conducted at Riyue and Chebu PV plants,with small-scale UAV cleaning validation in Chebu fish ponds.Results demonstrated that the system achieves efficient task scheduling,fault detection,and localized cleaning,thereby improving O&M efficiency,reducing costs,and enabling digitalized and intelligent management for large-scale PV stations.
文摘The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging(LIDAR)and HD maps with high level annotations.In this paper,we propose a scalable and affordable data collection and annotation framework image-to-map annotation proximity(I2MAP),for affordance learning in autonomous driving applications.We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map(OSM).Our benchmark consists of 40000 images with more than40 affordance labels under various day time and weather even with very challenging heavy snow.We implemented sample advanced driver-assistance systems(ADAS)functions by training our data with neural networks(NN)and cross-validate the results on benchmarks like KITTI and BDD100K,which indicate the effectiveness of our framework and training models.
基金supported in part by the Key-Area Researchand Development Program of Guangdong Province(2020B0909050003)the Program of Jiangxi(20204ABC03A13)。
文摘Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.
文摘The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread use.Over-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.
基金supported in part by the National Key Research and Development(No.2021YFB2501200).
文摘Virtual simulation testing of Autonomous Vehicles(AVs)is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs.Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets.However,the criticalities defined in their testing tasks are based on fixed assumptions,the obtained scenarios cannot pose a challenge to AVs with different strategies.To fill this gap,we propose an intelligent testing method based on operable testing tasks.We found that the driving behavior of Surrounding Vehicles(SVs)has a critical impact on AV,which can be used to adjust the testing task difficulty to find more challenging scenarios.To model different driving behaviors,we utilize behavioral utility functions with binary driving strategies.Further,we construct a vehicle interaction model,based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty.Finally,by adjusting SV’s strategies,we can generate more corner cases when testing different AVs in a finite number of simulations.
