A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Pavement distress detection using convolutional neural networks with images captured via UAV
Abstract Pavement distress detection is crucial in the decision-making for maintenance planning. Unmanned aerial vehicles (UAVs) are helpful in collecting pavement images. This paper proposes the collection of pavement distress information using a UAV with a high-resolution camera. A UAV platform for pavement image collection was assembled, and the flight settings were studied for optimal image quality. The collected images were processed and annotated for model training. Three state-of-the-art object-detection algorithms—Faster R-CNN, YOLOv3, and YOLOv4, were used to train the dataset, and their prediction performances were compared. A pavement image dataset was established with six types of distress. YOLOv3 demonstrated the best performance of the three algorithms, with a mean average precision (MAP) of 56.6%. The findings of this study assist in the inspection of non-destructive automatic pavement conditions.
Highlights UAV flight parameters are examined for pavement image collection. An UAV pavement image dataset (UAPD) was established. Anchor size is researched for pavement distress detection. YOLOv3 outperforms YOLOv4 and Faster R-CNN.
Pavement distress detection using convolutional neural networks with images captured via UAV
Abstract Pavement distress detection is crucial in the decision-making for maintenance planning. Unmanned aerial vehicles (UAVs) are helpful in collecting pavement images. This paper proposes the collection of pavement distress information using a UAV with a high-resolution camera. A UAV platform for pavement image collection was assembled, and the flight settings were studied for optimal image quality. The collected images were processed and annotated for model training. Three state-of-the-art object-detection algorithms—Faster R-CNN, YOLOv3, and YOLOv4, were used to train the dataset, and their prediction performances were compared. A pavement image dataset was established with six types of distress. YOLOv3 demonstrated the best performance of the three algorithms, with a mean average precision (MAP) of 56.6%. The findings of this study assist in the inspection of non-destructive automatic pavement conditions.
Highlights UAV flight parameters are examined for pavement image collection. An UAV pavement image dataset (UAPD) was established. Anchor size is researched for pavement distress detection. YOLOv3 outperforms YOLOv4 and Faster R-CNN.
Pavement distress detection using convolutional neural networks with images captured via UAV
Zhu, Junqing (author) / Zhong, Jingtao (author) / Ma, Tao (author) / Huang, Xiaoming (author) / Zhang, Weiguang (author) / Zhou, Yang (author)
2021-10-01
Article (Journal)
Electronic Resource
English
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