Automated detection of airfield pavement damages: an efficient light-weight algorithm

ABSTRACT

Fast and accurate detection of airfield pavement damage is crucial to airport flight safety and airfield pavement maintenance. An efficient and lightweight detection algorithm that can be embedded into the mobile detection device has been in urgent demand. However, traditional Convolutional Neural Networks (CNNs) usually generate redundant feature maps during feature extraction or use extra operations during feature fusion to gain better performance, which greatly challenges the efficiency of the algorithm. We approached this issue by proposing an accurate and efficient detection algorithm, the YOLOv5-APD. The algorithm improves the model performance in two ways: Speeding up training and inferencing by using cheaper operations during feature extraction; Reducing the model complexity by removing redundant nodes during feature fusion. We verified the detection performance of YOLOv5-APD on a self-made dataset and compared it with the other state-of-the-art (SOTA) models. Then ablation experiments were carried out to investigate the effects of the proposed model design and the impact of image augmentation. Results showed that the proposed YOLOv5-APD model outperformed the SOTA algorithms in model performance and efficiency, which attained the optimal performance mean average precision (mAP) of 0.924. The proposed model also achieved the fastest inference speed of 142 frame-per-second (FPS), with a model footprint of 8.3 G FLOPs and 8 MB Parameters.

KEYWORDS:

• Airfield pavement
• automatic damage detection
• deep learning
• data augmentation
• YOLO

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is sponsored by the National Natural Science Foundation of China (No. 52008311 and No. 51878499), the Science and Technology Commission of Shanghai Municipality (No. 21ZR1465700 and No. 19DZ1204200), Shandong Province Transportation Science and Technology Plan Project (2021B47), the Fundamental Research Funds for the Central Universities (22120230196), and Shanghai Municipal Natural Science Foundation (21ZR1465700). The authors are grateful for their financial support.