FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images (original) (raw)
Related papers
Springer Nature , 2024
Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score.
Structural Crack Detection and Classification using Deep Convolutional Neural Network
Pakistan Journal of Engineering and Technology
Cracks are indicators that affect the stability and integrity of infrastructures. Fast, reliable, and cost-effective crack detection methods are required to overcome the shortcomings of traditional approaches. This paper works on a transfer learning approach based on the deep convolutional neural network model VGG19 to detect cracks. Further, the proposed method is based on an improved VGG-19 model. The experiment is carried out on the SDNET2018 annotated images dataset. The dataset comprises of total 15k images, training set consists of 5000 cracked and 5000 un-cracked images of walls, pavements, and bridges. The experimental results on the proposed model provide 91.8% accuracy in detecting cracks on the testing set. The paper concluded that fine-tuning of the VGG19 (Visual Geometry Group) model accomplish satisfactory results in detecting cracks on images of multiple infrastructures.
Influence of image noise on crack detection performance of deep convolutional neural networks
ArXiv, 2021
Development of deep learning techniques to analyse image data is an expansive and emerging field. The benefits of tracking, identifying, measuring, and sorting features of interest from image data has endless applications for saving cost, time, and improving safety. Much research has been conducted on classifying cracks from image data using deep convolutional neural networks; however, minimal research has been conducted to study the efficacy of network performance when noisy images are used. This paper will address the problem and is dedicated to investigating the influence of image noise on network accuracy. The methods used incorporate a benchmark image data set, which is purposely deteriorated with two types of noise, followed by treatment with image enhancement pre-processing techniques. These images, including their native counterparts, are then used to train and validate two different networks to study the differences in accuracy and performance. Results from this research re...
Materials
Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and...
DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection
IEEE Transactions on Image Processing, 2019
Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which brings great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep con-volutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger-scale feature maps and more holistic representations are made in smaller-scale feature maps. We build DeepCrack net on the encoder-decoder architecture of SegNet, and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves F-Measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.
Crack Detection using Deep Learning
IRJET, 2023
Concrete cracks are one of the primary signs of a structure that would cause major harm to the entire infrastructure. The conventional method of addressing cracks is manual inspection. We discovered during the survey that there are numerous existing technologies, including approaches based on computer vision and image processing. There are several techniques for locating fractures on different surfaces utilizing manual inputs. Different factors influenced the performance of traditional models, which provided results with variable degrees of accuracy. Because there are many different strategies that can be used to produce a good crack detection output, the performance and efficiency were influenced by the environment. Using a Convolutional Neural Network, we have constructed a deep learning model for fracture classification and segmentation. Convolution, activation, and pooling layers make up the foundation of convolutional neural networks. These layers enhance the performance and generalization of neural networks by extracting picture information, introducing nonlinearity, and reducing feature dimensionality. The observed model's accuracy was between 97 and 98 percent. This model can be used to keep track of the condition of the concrete surfaces of bridges, tunnels, and other public transportation infrastructure.
Crack Detection Using Enhanced Hierarchical Convolutional Neural Networks
Proceedings of Australasian Conference on Robotics and Automation 2019 (ACRA), Adelaide, Australia, 2019
Unmanned aerial vehicles (UAV) are expected to replace human in hazardous tasks of surface inspection due to their flexibility in operating space and capability of collecting high quality visual data. In this study, we propose enhanced hierarchical convolutional neu-ral networks (HCNN) to detect cracks from image data collected by UAVs. Unlike traditional HCNN, here a set of branch networks is utilised to reduce the obscuration in the down-sampling process. Moreover, the feature preserving blocks combine the current and previous terms from the convolutional blocks to provide input to the loss functions. As a result, the weights of resized images can be reduced to minimise the information loss. Experiments on images of different crack datasets have been carried out to demonstrate the effectiveness of proposed HCNN.
Robust Deep Representation Learning for Road Crack Detection
2021 The 5th International Conference on Video and Image Processing, 2021
Computer vision (CV) based inspection has recently attracted considerable attention and is progressively replacing traditional visual inspection which is subject to poor accuracy, high subjectivity, and inefficiency. This paper, benefiting from hybrid structures of multichannel parallel convolutional neural networks (pCNNs), introduces a unique deep learning framework for road crack detection. Ideally, CNN-based frameworks require relatively huge computing resources for accurate image analysis. However, the portability objective of this work necessitates the utilization of low-power processing units. To that purpose, we propose robust deep representation learning for Road Crack Detection (RoCDe) which uses multichannel pCNNs. Bayesian optimization algorithm (BOA) was used to optimize the multichannel pCNNs training with the fewest possible neural network (NN) layers to achieve maximum accuracy, improved efficiency, and minimum processing time. The CV training was done using two distinct optimizers namely Adam and RELU on a sufficiently available dataset through image preprocessing and data augmentation. Experimental results show that the proposed algorithm can achieve high accuracy around 95% in crack detection, which is good enough to replace human inspections normally conducted on-site. This is largely due to well-calibrated predictive uncertainty estimates (WPUE). The effectiveness of the proposed model is demonstrated and validated empirically via extensive experiments and rigorous evaluation on large scale real-world datasets. Furthermore, the performance of hybrid CNNs is compared with state-of-the-art NN models, and the results pro- vides remarkable difference in success level, proving the strength of multichannel pCNNs.
A Cost Effective Solution for Road Crack Inspection using Cameras and Deep Neural Networks
arXiv: Computer Vision and Pattern Recognition, 2019
Automatic crack detection on pavement surfaces is an important research field in the scope of developing an intelligent transportation infrastructure system. In this paper, a cost effective solution for road crack inspection by mounting commercial grade sport camera, GoPro, on the rear of the moving vehicle is introduced. Also, a novel method called ConnCrack combining conditional Wasserstein generative adversarial network and connectivity maps is proposed for road crack detection. In this method, a 121-layer densely connected neural network with deconvolution layers for multi-level feature fusion is used as generator, and a 5-layer fully convolutional network is used as discriminator. To overcome the scattered output issue related to deconvolution layers, connectivity maps are introduced to represent the crack information within the proposed ConnCrack. The proposed method is tested on a publicly available dataset as well our collected data. The results show that the proposed method...