PRECISION COMPARISON OF DEEP LEARNING MODELS FOR DETECTING CONCRETE SURFACE DETERIORATION TYPES FROM DIGITAL IMAGES

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  • ANAI Satoshi
    元 大阪大学 大学院工学研究科 環境・エネルギー工学専攻
  • YABUKI Nobuyoshi
    大阪大学 大学院工学研究科 環境エネルギー工学専攻
  • FUKUDA Tomohiro
    大阪大学 大学院工学研究科 環境エネルギー工学専攻

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  • 画像上のコンクリート表面の変状検出に関する複数の深層学習モデルの精度比較

Abstract

<p> Visual inspection of concrete structures is an important task; however, it is labor-intensive work, taking much time and effort, and it is often dangerous when inspectors work in high places. Automatic detection of deterioration such as cracks, free lime, exposed reinforcing bars, etc., from digital images has been extensively researched. Current deep learning approaches have improved the detection performance compared with conventional approaches. Most deep learning approaches use publicly available deep learning models, such as Faster R-CNN and Single Shot multiBox Detector (SSD). Although the results of deterioration detection methods have been compared with the conventional approach, the deep learning models themselves have not been compared. In this research, using the same datasets, seven deep learning models (YOLOv3, RetinaNet-50, RetinaNet-101, RetinaNet-152, SSD512, SSD300, and Faster R-CNN), were compared for detecting five types of deterioration (cracks, exposed reinforcing bars, free lime c-type, free lime d-type, and free lime e-type). YOLOv3 showed the highest mean average precision (mAP) of 85.7%, whereas the other models showed less than 80%.</p>

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