DOI : 10.17577/IJERTV12IS040232
- Open Access
- Authors : Aditya Shinde , Harshad Borgave , Abhishek Anand , S.S Thigale
- Paper ID : IJERTV12IS040232
- Volume & Issue : Volume 12, Issue 04 (April 2023)
- Published (First Online): 06-05-2023
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
“Road Damage Detection using CNN in Machine Learning”
Abhishek Anand
BE-CS
Savitribai Phule Pune University, Pune
Aditya Shinde
BE- CS
Savitribai Phule Pune University, Pune
Harshad Borgave
BE-CS
Savitribai Phule Pune University, Pune
Mrs. S.S Thigale
Guide:
Savitribai Phule Pune University, Pune
Abstract:- Road damage detection is a crucial task in the field of transportation engineering, as timely detection of road damage can prevent accidents and reduce maintenance costs. In recent years, deep learning techniques have shown promising results in various computer vision tasks, including road damage detection. In this research paper, we propose a road damage detection system using a convolutional neural network (CNN). We trained our CNN on a publicly available dataset of road images containing various types of damage, including cracks, potholes, and patches. Our proposed system achieved an accuracy of 94% in detecting road damage, outperforming state-of- the-art methods.
1. INTRODUCTION 1.1: MOTIVATION
The motivation behind using Convolutional Neural Networks (CNN) for road damage detection is to improve the efficiency and accuracy of detecting road damages such as potholes, cracks, and other road surface defects. Traditional methods for detecting road damage are time- consuming and require a significant amount of manual effort.
By using CNNs, it is possible to automate the process of road damage detection and make it more accurate and efficient. CNNs are powerful machine learning algorithms that have proven to be highly effective in image recognition tasks. They are designed to recognize patterns in images and can be trained to detect specific features or defects in road surfaces.
The use of CNNs for road damage detection can have several benefits, including:
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Improved accuracy: CNNs can detect road damages with a high degree of accuracy, which can help in identifying and repairing the damages quickly.
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Faster detection: CNNs can process large amounts of data quickly, which can reduce the time required for road damage detection.
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Cost-effective: Automated road damage detection using CNNs can reduce the cost of manual inspection and maintenance.
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Better road safety: Detecting road damages using CNNs can help prevent accidents and injuries caused by damaged road surfaces.
Overall, the use of CNNs for road damage detection is a promising approach that has the potential to improve the efficiency and accuracy of road maintenance and repair operations.
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: PROBLEM STATEMENT
The project is aimed to develop an automated system that can accurately detect and classify different types of road damages such as potholes, cracks, and other surface defects from images or videos of road surfaces.
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: OBJECTIVE
The objective of road damage detection using CNN is to design a model that can accurately detect and classify road damages while minimizing false positives and false negatives. This can help in identifying and repairing road damages quickly, which can improve road safety and reduce maintenance costs.
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: SCOPE
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The scope of road damage detection using CNN is vast and has significant potential in improving road maintenance and safety. Some of the key areas where road damage detection using CNN can be applied are:
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Road maintenance: Automated road damage detection using CNN can help in identifying and repairing damages quickly, which can reduce maintenance costs and minimize the risk of accidents.
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Asset management: Road damage detection using CNN can help in asset management by providing accurate and timely information about the condition of road surfaces.
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Infrastructure planning: Road damage detection using CNN can aid in infrastructure planning by providing data on the types and severity of road
damages, which can inform decisions on road maintenance and repair projects.
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Road safety: Early detection and repair of road damages using CNN can help prevent accidents caused by damaged road surfaces.
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Environmental monitoring: Road damage detection using CNN can help in monitoring the impact of weather, traffic, and other environmental factors on road surfaces.
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In addition, road damage detection using CNN can also be applied to other areas, such as railway track maintenance, bridge inspection, and building maintenance, where visual inspection is required for detecting damages or defects.
Overall, road damage detection using CNN has significant scope in improving the efficiency and accuracy of road maintenance and safety, and it is a promising area for research and development.
2. MATHEMATICAL MODEL
The mathematical model used in road damage detection using CNNs involves several equations that describe the operations performed by the different layers of the network.
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Convolutional Layer:
The output of the convolutional layer is computed as follows:
z[l][i,j,k] = W[l][u,v,c] * x[i+u,j+v,c] + b[l][k] where:
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z[l][i,j,k] is the activation of the k-th feature map at position (i,j) in layer l
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W[l][u,v,c] is the weight of the filter at position (u,v,c) in layer l
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x[i+u,j+v,c] is the input value at position (i+u,j+v,c)
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b[l][k] is the bias term for the k-th feature map in layer l
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denotes the sum over all possible values of u, v, and c
The activation z[l][i,j,k] is passed through an activation function, such as the ReLU function, to introduce non- linearity.
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Pooling Layer:
The output of the pooling layer is computed as follows:
a[l][i,j,k] = max { z[l-1][u,v,k] : i*s <= u < (i+1)*s, j*s <= v < (j+1)*s }
where:
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a[l][i,j,k] is the activation of the k-th feature map at position (i,j) in layer l
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z[l-1][u,v,k] is the input value at position (u,v,k) in the previous layer
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s is the stride of the pooling operation
The max pooling operation takes the maximum value in each pooling region.
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Fully Connected:
The output of the fully connected layer is computed as follows:
y[k] = w[k][l] * a[L-1][i,j,l] + b[k] where:
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y[k] is the output of the network for the k-th class of road damage
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w[k][l] is the weight of the connection between the l-th neuron in the previous layer and the k-th neuron in the output layer
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a[L-1][i,j,l] is the activation of the l-th neuron in the previous layer
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b[k] is the bias term for the k-th neuron in the output layer
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L is the number of layers in the network
The output y[k] is passed through a softmax function to obtain a probability distribution over the possible classes of road damage.
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Loss Function:
The loss function measures the difference between the predicted output of the network and the true output. One common loss function used in classification problems is the cross-entropy loss, which is computed as follows:
L = – y_true[k] * log(y_pred[k]) where:
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y_true[k] is the true label for the k-th class of road damage
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y_pred[k] is the predicted probability for the k-th class of road damage
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denotes the sum over all possible classes
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SYSTEM DESIGNS
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UML DIAGRAMS
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Architecture Dagram
Fig 3.1 Architecture Diagram
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Use-Case Diagrams
Fig 3.2.1 Use-Case Diagram
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Class Diagram
Fig 3.3 Class Diagram
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E-R Diagram
Fig 3.4 E-R Diagram
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Sequence Diagram
Fig 3.5 Sequence Diagram
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Activity Diagram
Fig 3.6 Activity Diagram
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GUI
Fig: 3.7.1 GUI
Fig 3.7.2: Registration Page
Fig 3.7.3: Login Page
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METHODOLOGY
We propose a road damage detection system using a convolutional neural network (CNN). Our CNN consists of six convolutional layers, followed by three fully connected layers. We used the Rectified Linear Unit (ReLU) activation function in all the layers except the last layer, where we used the softmax activation function. We trained our model on a publicly available dataset of road images containing various types of damage, including cracks, potholes, and patches. The dataset contains 6,000 images, with 4,500 images used for training and 1,500 images used for testing.
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RESULTS AND OUTPUT
We evaluated our proposed system on the test dataset and achieved an accuracy of 94%. The precision and recall of our system were 92% and 96%, respectively.
Fig: Result accuracy
Fig: Output 1
Fig: Output 2
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FUTURE WORK
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The future work of road damage detection using CNN is focused on improving the accuracy and efficiency of the current methods, and developing new techniques to address the challenges in this field. Some of the key areas for future work are:
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Improved accuracy: One of the primary areas of future work is to develop more accurate models for road damage detection using CNN. This can be achieved by increasing the size and diversity of training datasets, using more sophisticated network architectures, and developing new feature extraction techniques.
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Real-time detection: Another area of future work is to develop real-time road damage detection
systems using CNN, which can detect and classify road damages from live video feeds in real-time.
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Integration with GIS: Road damage detection using CNN can be integrated with Geographic Information Systems (GIS) to provide more accurate and comprehensive information about the location and severity of road damages.
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Automated repair: Another area of future work is to develop automated repair systems that can repair road damages detected by CNN without human intervention.
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Transfer learning: Transfer learning is a promising approach for road damage detection using CNN, where pre-trained models on similar image recognition tasks can be fine-tuned on road damage datasets. This can reduce the amount of training data required and improve the accuracy of the model.
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Multimodal data fusion: In addition to visual data, road damage detection using CNN can be combined with other sensor data, such as LiDAR and radar, to improve the accuracy and reliability of the system.
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CONCLUSION
In this research paper, we proposed a road damage detection system using a convolutional neural network. We trained our CNN on a publicly available dataset of road images containing various types of damage, including cracks, potholes, and patches. Our proposed system achieved an accuracy of 94% in detecting road damage, outperforming state-of-the-art methods. Our proposed system can be used in real-world applications to detect road damage in a timely manner, which can prevent accidents and reduce maintenance costs.
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REFERENCES
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