Accurate and timely surveying of airfield pavement distress is crucial for cost-effective air-port maintenance.Deep learning(DL)approaches,leveraging advancements in computer science and image acquisition techniques,h...Accurate and timely surveying of airfield pavement distress is crucial for cost-effective air-port maintenance.Deep learning(DL)approaches,leveraging advancements in computer science and image acquisition techniques,have become the mainstream for automated air-field pavement distress detection.However,fully-supervised DL methods require a large number of manually annotated ground truth labels to achieve high accuracy.To address the challenge of limited high-quality manual annotations,we propose a novel end-to-end distress detection model called class activation map informed weakly-supervised dis-tress detection(WSDD-CAM).Based on YOLOv5,WSDD-CAM consists of an efficient back-bone,a classification branch,and a localization network.By utilizing class activation map(CAM)information,our model significantly reduces the need for manual annotations,auto-matically generating pseudo bounding boxes with a 71%overlap with the ground truth.To evaluate WSDD-CAM,we tested it on a self-made dataset and compared it with other weakly-supervised and fully-supervised models.The results show that our model achieves 49.2%mean average precision(mAP),outperforming other weakly-supervised methods and even approaching state-of-the-art fully-supervised methods.Additionally,ablation experiments confirm the effectiveness of our architecture design.In conclusion,our WSDD-CAM model offers a promising solution for airfield pavement distress detection,reducing manual annotation time while maintaining high accuracy.This efficient and effec-tive approach can significantly contribute to cost-effective airport maintenance management.展开更多
This paper discusses cracking in airport pavements as studied in Construction Cycle 6 of testing carried out at the National Airport Pavement Testing Facility by the Federal Aviation Administration. Pavements of three...This paper discusses cracking in airport pavements as studied in Construction Cycle 6 of testing carried out at the National Airport Pavement Testing Facility by the Federal Aviation Administration. Pavements of three different flexural strengths as well as two different subgrades, a soft bituminous layer and a more rigid layer known as econocrete, were tested. In addition to this, cracking near two types of isolated transition joints, a reinforced edge joint and a thickened edge joint, was considered. The pavement sections were tested using a moving load simulating that of an aircraft. It has been determined that the degree of cracking was reduced as the flexural strength of the pavement was increased and that fewer cracks formed over the econocrete base than over the bituminous base. In addition, the thickened edge transition joint was more effective in preventing cracking at the edges compared to the reinforced edge joint.展开更多
基金support of the National Natural Science Foundation of China(Nos.52008311,51878499,and 52178433)the Science and Technology Commission of Shanghai Municipality(No.21ZR1465700)the Fundamental Research Funds for the Central Universities(No.22120230196).
文摘Accurate and timely surveying of airfield pavement distress is crucial for cost-effective air-port maintenance.Deep learning(DL)approaches,leveraging advancements in computer science and image acquisition techniques,have become the mainstream for automated air-field pavement distress detection.However,fully-supervised DL methods require a large number of manually annotated ground truth labels to achieve high accuracy.To address the challenge of limited high-quality manual annotations,we propose a novel end-to-end distress detection model called class activation map informed weakly-supervised dis-tress detection(WSDD-CAM).Based on YOLOv5,WSDD-CAM consists of an efficient back-bone,a classification branch,and a localization network.By utilizing class activation map(CAM)information,our model significantly reduces the need for manual annotations,auto-matically generating pseudo bounding boxes with a 71%overlap with the ground truth.To evaluate WSDD-CAM,we tested it on a self-made dataset and compared it with other weakly-supervised and fully-supervised models.The results show that our model achieves 49.2%mean average precision(mAP),outperforming other weakly-supervised methods and even approaching state-of-the-art fully-supervised methods.Additionally,ablation experiments confirm the effectiveness of our architecture design.In conclusion,our WSDD-CAM model offers a promising solution for airfield pavement distress detection,reducing manual annotation time while maintaining high accuracy.This efficient and effec-tive approach can significantly contribute to cost-effective airport maintenance management.
基金the Federal Aviation Administration (FAA) as this work is funded under FAA research grant #10-G-012project has been sponsored by the FAA
文摘This paper discusses cracking in airport pavements as studied in Construction Cycle 6 of testing carried out at the National Airport Pavement Testing Facility by the Federal Aviation Administration. Pavements of three different flexural strengths as well as two different subgrades, a soft bituminous layer and a more rigid layer known as econocrete, were tested. In addition to this, cracking near two types of isolated transition joints, a reinforced edge joint and a thickened edge joint, was considered. The pavement sections were tested using a moving load simulating that of an aircraft. It has been determined that the degree of cracking was reduced as the flexural strength of the pavement was increased and that fewer cracks formed over the econocrete base than over the bituminous base. In addition, the thickened edge transition joint was more effective in preventing cracking at the edges compared to the reinforced edge joint.
