# Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

- (1)
- The pothole dataset was contrast-enhanced by color adjustment, and geometric transformation was adopted to expand the number of samples. A data augmentation strategy suitable for potholes was proposed to train DL models.
- (2)
- The ResNet101 network was used to improve the feature extraction network of YOLOv3. Complete intersection over union (CIoU) was applied to measure the loss of the proposed model. The anchor sizes were modified by the K-Means++ algorithm. An object detection model applicable for potholes was established.
- (3)
- Adversarial samples of potholes were generated by random occlusion and adding noise before testing, which verified the robustness of this model.

## 2. Methodology

#### 2.1. Pavement Pothole Dataset

#### 2.2. Pothole Data Pre-Processing

#### 2.2.1. Color Adjustment

**X**= (

**x**

_{H},

**x**

_{S},

**x**

_{I})

^{T}is a vector of color pixels representing each image. F(∙) is a probability function, and F(

**Z**) = F(x

_{I}, x

_{S}) = P{

**x**

_{I}≤ x

_{I}

_{,}

**x**

_{S}≤ x

_{S}}.

^{2}R(x, y), ▽

^{2}G(x, y), and▽

^{2}B(x, y) are the Laplace operators of each component (red, green, and blue) of the color images, respectively.

#### 2.2.2. Geometric Transformation

_{c}represents several cases of learning feature information in extreme cases. For example, X

_{c}

^{(1)}= (1·F, 0·B) is the case when F weighs 1, while B is 0.

#### 2.3. Object Detection

#### 2.3.1. YOLOv3–ResNet101 Structure

#### 2.3.2. Loss Function

^{gt}represents the ground truth, and IoU is defined as the ratio of intersection and union between P, P

^{gt}, which reflects the degree of overlap between the two. However, there are two problems with this calculation: first, when the prediction box is not overlapped with the real box, the IoU is 0, and the gradient return cannot be carried out; second, the calculation result is only related to the overlap area and cannot measure the way of intersection between two boxes. Therefore, the IoU is not a comprehensive and accurate measure of the degree of overlap.

^{gt}is directly minimized to speed up convergence in distance IoU (DIoU); nevertheless, the aspect ratio of the two is not considered.

^{gt}and h

^{gt}are the width and length of P

^{gt}, respectively, and w and h are the width and length of P, respectively.

#### 2.3.3. Anchor Size

#### 2.3.4. Robustness Verification

_{e}, y

_{e}are random coefficients, and Z

_{e}is the random occlusion area. The pixel color in the random occlusion area was set to zero.

#### 2.3.5. Evaluation Index

_{b}refers to the number of samples in the b-th bin. N is the sum of samples. A(b) is the average value of the ground truth of the samples in the b-th bin. Moreover, confidence(b) represents the average value of the model’s predicted probabilities in the b-th bin. A smaller difference between A(b) and confidence(b) indicates a higher model confidence.

## 3. Results and Discussion

#### 3.1. Results of Data Augmentation

#### 3.2. Evaluation of Different DL Models

#### 3.3. Robustness Analysis

#### 3.4. Generalization Performance Analysis

## 4. Conclusions

- (1)
- An effective image preprocessing strategy for improving and expanding the pothole dataset was proposed using the methods of color adjustment and geometric transformation, which ensured the detection stability of the proposed model.
- (2)
- The potholes were further subdivided according to whether there was water. The detection results could preliminarily judge the surface state of the pothole and weather conditions.
- (3)
- The ResNet101 network was adopted to extract features in the YOLOv3 model, which obtained abundant information on potholes. The modified anchor sizes based on the K-Means++ method were more in line with the shapes and sizes of the pothole, which improved the accuracy of the identification and location. The loss function defined by CIoU was of great help for accurate pothole detection.
- (4)
- The robustness of the proposed model was verified by generating adversarial attack samples through random occlusion and adding noise. Results showed that the overall robustness was good. Specifically, the proposed model was more robust to Gaussian noise under the interference intensity.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Comparison of ResNet101 network structure with other residual networks (ResNet18, ResNet34, and ResNet50).

**Figure 10.**Evaluation indices of YOLOv3 model and other improved models. (

**a**) overall evaluation indices; (

**b**) evaluation indices of P1 and P2 labels.

Camera Features | Details | Camera Features | Details |
---|---|---|---|

Type | Basler raL2048-80km | Power supply requirements (typical value) | 3 W |

Interface | Camera Link | Type of light-sensitive chips | CMOS |

Resolution ratio | 3854 px × 2065 px | Size of light-sensitive chips | 14.3 mm |

Dataset | Training Dataset | Validation Dataset | Testing Dataset |
---|---|---|---|

Image with potholes | 480 | 160 | 160 |

Image without potholes | 720 | 240 | 240 |

Total | 1200 | 400 | 400 |

Pre-Processing Steps | Original Image | Color Transformation (Contrast Adjustment) | Geometric Transformation (Data Augmentation) | ||||
---|---|---|---|---|---|---|---|

Contrast | Sharpness | Rotating 90° Anticlockwise | Rotating 90° Clockwise | Rotating 180° | Random Crop | ||

Potholes with water | |||||||

Potholes without water |

Network | Layer Name | Output Size | Parameters |
---|---|---|---|

ResNet101 | conv1 | 512 × 512 × 3 | conv, 7 × 7 × 64, stride 2 |

conv2 | 256 × 256 × 64 | >max pool, 3 × 3, stride 2 bottleneck: 1 × 1 $\left[\begin{array}{c}1\times 1\times 64\\ 3\times 3\times 64\\ 1\times 1\times 256\end{array}\right]\times 3$ | |

conv3 | 128 × 128 × 256 | bottleneck: 1 × 1 $\left[\begin{array}{c}1\times 1\times 128\\ 3\times 3\times 128\\ 1\times 1\times 512\end{array}\right]\times 4$ | |

conv4 | 64 × 64 × 512 | bottleneck: 1 × 1 $\left[\begin{array}{c}1\times 1\times 256\\ 3\times 3\times 256\\ 1\times 1\times 1024\end{array}\right]\times 23$ | |

conv5 | 32 × 32 × 1024 | bottleneck: 1 × 1 $\left[\begin{array}{c}1\times 1\times 512\\ 3\times 3\times 512\\ 1\times 1\times 2048\end{array}\right]\times 3$ | |

YOLOv3 | Y_{1} | 32 × 32 × 1024 | 3 anchors $\left[\begin{array}{c}3\times 3\times 1024\\ 1\times 1\times 1024\end{array}\right]\times 2$ |

Y_{2} | 64 × 64 × 512 | 3 anchors $\left[\begin{array}{c}3\times 3\times 1024\\ 1\times 1\times 1024\end{array}\right]\times 2$ | |

Y_{3} | 128 × 128 × 256 | 3 anchors $\left[\begin{array}{c}3\times 3\times 1024\\ 1\times 1\times 1024\end{array}\right]\times 2$ |

Detection Scale | Anchor Size | ||
---|---|---|---|

Scale 1: 32 × 32 | (176, 312) | (335, 247) | (285, 362) |

Scale 2: 64 × 64 | (126, 236) | (285.5, 132) | (213, 201) |

Scale 3: 128 × 128 | (34, 35) | (72, 79) | (149, 126) |

Hyperparameters | Value |
---|---|

Batch size | 2 |

Epochs | 100 |

Learning rate | 0.00125 |

Weight decay | L2 |

Optimizer | Momentum |

Momentum | 0.9 |

**Table 7.**Evaluation index results of YOLOv3 model and proposed model before and after data augmentation.

Evaluation Indices | P | R | mAP | F1 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

P1 | P2 | Total | P1 | P2 | Total | P1 | P2 | Total | P1 | P2 | Total | |

YOLOv3 | 0.734 | 0.677 | 0.705 | 0.742 | 0.693 | 0.718 | 0.749 | 0.704 | 0.727 | 0.737 | 0.685 | 0.711 |

YOLOv3 (aug) | 0.764 | 0.720 | 0.742 | 0.775 | 0.720 | 0.747 | 0.771 | 0.749 | 0.760 | 0.769 | 0.720 | 0.744 |

Proposed model | 0.802 | 0.784 | 0.793 | 0.830 | 0.773 | 0.801 | 0.853 | 0.838 | 0.845 | 0.816 | 0.780 | 0.798 |

Proposed model (aug) | 0.872 | 0.866 | 0.869 | 0.882 | 0.840 | 0.861 | 0.895 | 0.892 | 0.893 | 0.877 | 0.852 | 0.865 |

Evaluation Indices | Faster R-CNN [49] | Cascade R-CNN [50] | SSD [51] | YOLOv3–DarkNet53 [26] | YOLOv3–ResNet101 | Proposed Model | |
---|---|---|---|---|---|---|---|

P | P1 | 0.807 | 0.800 | 0.747 | 0.764 | 0.771 | 0.872 |

P2 | 0.817 | 0.804 | 0.751 | 0.720 | 0.806 | 0.866 | |

Total | 0.812 | 0.802 | 0.749 | 0.742 | 0.789 | 0.869 | |

R | P1 | 0.793 | 0.794 | 0.733 | 0.775 | 0.853 | 0.882 |

P2 | 0.822 | 0.810 | 0.738 | 0.720 | 0.804 | 0.840 | |

Total | 0.807 | 0.802 | 0.736 | 0.747 | 0.829 | 0.861 | |

mAP | P1 | 0.825 | 0.797 | 0.798 | 0.769 | 0.817 | 0.895 |

P2 | 0.814 | 0.805 | 0.751 | 0.749 | 0.831 | 0.892 | |

Total | 0.819 | 0.801 | 0.775 | 0.759 | 0.824 | 0.893 | |

F1 | P1 | 0.798 | 0.831 | 0.742 | 0.770 | 0.810 | 0.877 |

P2 | 0.800 | 0.803 | 0.744 | 0.720 | 0.805 | 0.852 | |

Total | 0.799 | 0.817 | 0.743 | 0.745 | 0.808 | 0.865 | |

AP50 | 0.819 | 0.801 | 0.775 | 0.759 | 0.824 | 0.893 | |

AP75 | 0.744 | 0.763 | 0.685 | 0.625 | 0.750 | 0.841 | |

AP90 | 0.782 | 0.783 | 0.730 | 0.692 | 0.784 | 0.867 |

Evaluation Indices | IoU_{50} | IoU_{75} | IoU_{90} | ||||||
---|---|---|---|---|---|---|---|---|---|

P1 | P2 | Total | P1 | P2 | Total | P1 | P2 | Total | |

P | 0.788 | 0.793 | 0.790 | 0.733 | 0.738 | 0.736 | 0.275 | 0.277 | 0.276 |

R | 0.763 | 0.802 | 0.783 | 0.718 | 0.746 | 0.732 | 0.266 | 0.248 | 0.257 |

mAP | 0.810 | 0.814 | 0.812 | 0.764 | 0.773 | 0.769 | 0.211 | 0.325 | 0.268 |

F1 | 0.775 | 0.797 | 0.786 | 0.737 | 0.743 | 0.740 | 0.240 | 0.238 | 0.239 |

Labels | Original Images | Proposed Model | Proposed Model (Random Occlusion) | Proposed Model (Salt-and-Pepper Noise Attacked) | Proposed Model (Gaussian Noise Attacked) |
---|---|---|---|---|---|

P1 (pothole with water) | |||||

P2 (pothole without water) | |||||

Original image | |||||

Detection results of the proposed model |

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## Share and Cite

**MDPI and ACS Style**

Wang, D.; Liu, Z.; Gu, X.; Wu, W.; Chen, Y.; Wang, L.
Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks. *Remote Sens.* **2022**, *14*, 3892.
https://doi.org/10.3390/rs14163892

**AMA Style**

Wang D, Liu Z, Gu X, Wu W, Chen Y, Wang L.
Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks. *Remote Sensing*. 2022; 14(16):3892.
https://doi.org/10.3390/rs14163892

**Chicago/Turabian Style**

Wang, Danyu, Zhen Liu, Xingyu Gu, Wenxiu Wu, Yihan Chen, and Lutai Wang.
2022. "Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks" *Remote Sensing* 14, no. 16: 3892.
https://doi.org/10.3390/rs14163892