Keisuke Maeda

Distress Classification of Road Structures via Adaptive Bayesian Network Model Selection

AbstractThis paper presents an accurate distress classification method via adaptive Bayesian network model selection for maintenance inspection of road structures. The main contribution of this pap...

Distress classification of road structures via decision level fusion

A distress classification method of road structures via decision level fusion is presented in this paper. In order to classify various kinds of distresses accurately, the proposed method integrates multiple classification results with considering their performance, and this is the biggest contribution of this paper. By introducing this approach, it becomes feasible to adaptively integrate the multiple classification results based on the accuracy of each classifier for a target sample. Consequently, realization of the accurate distress classification can be expected. Experimental results show that our method outperforms existing methods.

Automatic Martian Dust Storm Detection from Multiple Wavelength Data Based on Decision Level Fusion

This paper presents automatic Martian dust storm detection from multiple wavelength data based on decision level fusion. In our proposed method, visual features are first extracted from multiple wavelength data, and optimal features are selected for Martian dust storm detection based on the minimal-Redundancy-Maximal-Relevance algorithm. Second, the selected visual features are used to train the Support Vector Machine classifiers that are constructed on each data. Furthermore, as a main contribution of this paper, the proposed method integrates the multiple detection results obtained from heterogeneous data based on decision level fusion, while considering each classifier’s detection performance to obtain accurate final detection results. Consequently, the proposed method realizes successful Martian dust storm detection.

Bayesian network-based distress estimation using image features in road structure assessment

This paper presents a Bayesian network-based method for estimating a distress of road structures from inspection data. The distress is represented by a damage of road structures and its degree. In the previous work, the distress was estimated by utilizing Bayesian network based on categories of road structures, details of road structures and damaged parts. However, inspection data include not only the above items but also images of the distress. Therefore, by introducing the use of the images to the previous work, improvement of the distress estimation accuracy can be expected. The proposed method calculates Bayesian network from inspection items and their corresponding images to perform the distress estimation. Experimental results show the effectiveness of the proposed method.

Distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine

Abstract This paper presents distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine (CMWELM). For distress classification, it is necessary to extract semantic features that can effectively distinguish multiple kinds of distress from a small amount of class-imbalanced data. In recent machine learning techniques such as general deep learning methods, since effective feature transformation from visual features to semantic features can be realized by using multiple hidden layers, a large amount of training data are required. However, since the amount of training data of civil structures becomes small, it becomes difficult to perform successful transformation by using these multiple hidden layers. On the other hand, CMWELM consists of two hidden layers. The first hidden layer performs feature transformation, which can directly extract the semantic features from visual features, and the second hidden layer performs classification with solving the class-imbalanced problem. Specifically, in the first hidden layer, the feature transformation is realized by using projections obtained by maximizing the canonical correlation between visual and text features as weight parameters of the hidden layer without designing multiple hidden layers. Furthermore, the second hidden layer enables successful training of our classifier by using weighting factors concerning the class-imbalanced problem. Consequently, CMWELM realizes accurate distress classification from a small amount of class-imbalanced data.

Automatic detection of martian dust storms from heterogeneous data based on decision level fusion

This paper presents automatic detection of Martian dust storms from heterogeneous data (raw data, reflectance data and background subtraction data of the reflectance data) based on decision level fusion. Specifically, the proposed method first extracts image features from these data and selects optimal features for dust storm detection based on the minimal-Redundancy-Maximal-Relevance algorithm. Second, the selected image features are used to train the Support Vector Machine classifier that is constructed on each data. Furthermore, as a main contribution of this paper, the proposed method combines the multiple detection results obtained from the heterogeneous data based on decision level fusion with considering each classifiers detection performance to obtain accurate final detection results. Consequently, the proposed method realizes automatic and accurate detection of Martian dust storms.