While True: Learn() Mega Map Of Machine Learning Crack And Patch __HOT__
To address those technical challenges, we present an attention-based multi-scale CNN (A+MCNN) as a novel deep learning algorithm to classify pavement images with 11 different classes, including four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). The images are collected from both flexible and rigid pavement surfaces using four different high-speed line-scanning cameras to consider variations in camera properties and lighting calibrations. To cope with the variety of pavement objects, we design the A+MCNN to capture contextual information through multi-scale input tiles, as shown in Figure 1. Early fusion, mid-fusion, and late fusion are three approaches that are usually employed to fuse features extracted from multi-scale tiles. We employ an attention module as a mid-fusion strategy to adaptively combine multi-scale features based on their importance for the final prediction.
while True: learn() Mega Map of Machine Learning crack and patch
We also observe that the data imbalance affected the classification performance significantly. Joints, manholes, shoulders, crack seals, and potholes were the classes that represented less than 0.4% of each class in the UCF-PAVE 2017 (Group 1), while patches, curbing, markers, and cracks were the classes that made up between 2.4% and 1.6% (Group 2), as shown in Figure 7. The asphalt and concrete classes represented 90.6% of the dataset (Group 3). The analysis results show that the performance increased for the classes with a greater amount of data for all models: the average F-score was 0.720 for Group 1, 0.832 for Group 2, and 0.976 for Group 3. The multi-scale input and the mid-fusion process significantly improved the performance, especially for the classes with a smaller amount of data: for Group 1, the F-score was 0.507 with the S-CNNs, 0.824 with the M-CNNs, 0.840 with the MCNN-EarlyFusion, 0.870 with the MCNN-MidFusion, and 0.889 with the A+MCNN; for Group 2, the F-score was 0.747 with the S-CNNs, 0.864 with the M-CNNs, 0.867 with the MCNN-EarlyFusion, 0.915 with the MCNN-MidFusion, and 0.923 with the A+MCNN; and for Group 3, the F-score was 0.966 with the S-CNNs, 0.979 with the M-CNNs, 0.980 with the MCNN-EarlyFusion, 0.985 with the MCNN-MidFusion, and 0.987 with the A+MCNN. The above results show that the multi-scale input improved the classification performance significantly when the dataset was imbalanced. Among the M-CNNs, the M-VGG16 and M-VGG19 outperformed the M-ResNet50 and M-DenseNet121 slightly for Group 1: the F-score was 0.827 with the M-VGG16 and 0.836 with the M-VGG19, while it was 0.814 with the M-ResNet50 and 0.819 with the M-DenseNet121. The reason for this was that the deeper networks of the M-ResNet50 and M-DenseNet121 need more data to be properly trained. This limitation was mitigated by using the designed networks for Group 1: an F-score of 0.840 was found with the MCNN-EarlyFusion, 0.870 with the MCNN-MidFusion, and 0.889 with the A+MCNN. The results suggest that, with the given data, the customized network design worked better than the state-of-the-art deep networks.
A pothole can be easily misclassified as a patch, crack, or asphalt because it has pixels with a darker intensity and the existence of cracks around it. Since the A+MCNN calculates appropriate scores for the feature maps at each scale, the pothole can be distinguished accurately from other classes. The A+MCNN significantly outperformed both SCNNs and MCNNs, improving on the VGG16 by 18%, VGG19 by 19%, ResNet50 by 19%, and DenseNet121 by 18%, while improving on M-VGG16 by 6%, M-VGG19 by 2%, M-ResNet50 by 4%, M-DenseNet121 by 3%, and the MCNN-EarlyFusion by 9%.
Regression and classification are categorized under the same umbrella of supervised machine learning. The main difference between them is that the output variable in the regression is numerical (or continuous) while that for classification is categorical (or discrete).
Ans. Cross-validation is a technique which is used to increase the performance of a machine learning algorithm, where the machine is fed sampled data out of the same data for a few times. The sampling is done so that the dataset is broken into small parts of the equal number of rows, and a random part is chosen as the test set, while all other parts are chosen as train sets.
If we want a machine learning algorithm for the task of defect inspection, we feed it data such as images of rust or cracks. The corresponding annotation would be polygons for localization of those cracks or corrosion, and tags for naming them. 350c69d7ab