Efficient Pavement Crack Detection and Classification Using Custom YOLOv7 Model (original) (raw)
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Deep convolution neural network for crack detection on asphalt pavement
Journal of Physics: Conference Series, 2019
Asphalt cracks are one of the major road damage problems in civil field as it may potentially threaten the road and highway safety. Crack detection and classification is a challenging task because complicated pavement conditions due to the presence of shadows, oil stains and water spot will result in poor visual and low contrast between cracks and the surrounding pavement. In this paper, the network proposed a fully automated crack detection and classification using deep convolution neural network (DCNN) architecture. First, the image of pavement cracks manually prepared in RGB format with dimension of 1024x768 pixels, captured using NIKON digital camera. Next, the image will segmented into patches (32x32 pixels) as a training dataset from the original pavement cracks and trained DCNN with two different filter sizes: 3x3 and 5x5. The proposed method has successfully detected the presence of crack in the images with 98%, 99% and 99% of recall, precision and accuracy respectively. The...
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Image-Based Crack Detection Methods: A Review
Infrastructures
Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using ima...
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A Modified U-Net Architecture for Road Surfaces Cracks Detection
Proceedings of the 8th International Conference on Computing and Artificial Intelligence
Cracks on road surfaces causes inconveniences to drivers and passengers and may cause mechanical failure or even accidents. Good Road condition plays an important role in quick transportation of goods and services from one place to another and acts as a catalyst for the economic development. Road surfaces need to be maintained in good condition to ensure the safety of road users. Road damage detection is important for Structural Health Monitoring (SHM). Traditional manual inspection is normally performed through human visualization which is time consuming, expensive, dangerous because of the passing vehicles, suffers from subjective judgment of the inspector and pose difficulties in keeping records for future road maintenance and repair. The rapid emergency and development of AI has stimulated many experts to automate the process of crack detection through computer vision (CV) technology, though most of these studies faces challenge on getting good detection accuracy. In this study a novel modified U-Net Architecture for image classification and segmentation is proposed to detect cracks on the road surfaces by using detection and classification of the road images to determine whether they represent cracks or not. Extensive experiments are conducted on three publicly available road crack datasets to evaluate the performance of our proposed model, The performance of the proposed Modified U-Net architecture was verified with respect to different performance metrics such as accuracy, precision, recall and f1 score. Qualitative and Quantitative comparisons experimental results of the proposed approach were also compared with existing state of the art U-Net architectures. It can be inferred from results that the proposed approach achieves superior performance in terms of detection accuracy.
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Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture
Materials, 2020
Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature ...
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