Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail.

Parkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.

It has been recognized that deeper and wider neural networks are continuously advancing the state-of-the-art performance of various computer vision and machine learning tasks. However, they often require large sets of labeled data for effective training and suffer from extremely high computational complexity, preventing them from being deployed in real-time systems, for example vehicle object detection from vehicle cameras for assisted driving. In this paper, we aim to develop a fast deep neural network for real-time video object detection by exploring the ideas of knowledge-guided training and predicted regions of interest. Specifically, we will develop a new framework for training deep neural networks on datasets with limited labeled samples using cross-network knowledge projection which is able to improve the network performance while reducing the overall computational complexity significantly. A large pre-trained teacher network is used to observe samples from the training data. A projection matrix is learned to project this teacher-level knowledge and its visual representations from an intermediate layer of the teacher network to an intermediate layer of a thinner and faster student network to guide and regulate the training process. To further speed up the network, we propose to train a low-complexity object detection using traditional machine learning methods, such as support vector machine. Using this low-complexity object detector, we identify the regions of interest that contain the target objects with high confidence. We obtain a mathematical formula to estimate the regions of interest to save the computation for each convolution layer. Our experimental results on vehicle detection from videos demonstrated that the proposed method is able to speed up the network by up to 16 times while maintaining the object detection performance.

Parkinson's disease (PD) is the second most common neurological disease having no specific medical test for its diagnosis. In this study, we consider PD detection based on multimodal voice data that was collected through two channels, i.e., Smart Phone (SP) and Acoustic Cardioid (AC). Four types of data modalities were collected through each channel, namely sustained phonation (P), speech (S), voiced (V), and unvoiced (U) modality. The contributions of this paper are twofold. First, it explores optimal data modality and features having better information about PD. Second, it proposes a MultiModal Data–Driven Ensemble (MMDD-Ensemble) approach for PD detection. The MMDD-Ensemble has two levels. At the first level, different base classifiers are developed that are driven by multimodal voice data. At the second level, the predictions of the base classifiers are fused using blending and voting methods. In order to validate the robustness of the propose method, six evaluation measures, namely accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC), are adopted. The proposed method outperformed the best results produced by optimal unimodal framework from both the key evaluation aspects, i.e., accuracy and AUC. Furthermore, the proposed method also outperformed other state-of-the-art ensemble models. Experimental results show that the proposed multimodal approach yields 96% accuracy, 100% sensitivity, 88.88% specificity, 0.914 of MCC, and 0.986 of AUC. These results are promising compared to the recently reported results for PD detection based on multimodal voice data.

Industrial product surface defect detection is very important to guarantee high product quality and production efficiency. In this work, we propose a regression and classification based framework for generic industrial defect detection. Specifically, the framework consists of four modules: deep regression based detection model, pixel-level false positive reduction, connected component analysis and deep network for defect type classification. To train the detection model, we propose a high performance deep network structure and an algorithm to generate label data to capture the defect severity information from data annotation. We have tested the method on two public benchmark datasets, AigleRN and DAGM2007, and an in-house capacitor image dataset. The results have shown that our method can achieve the state-of-the-art performance in terms of detection accuracy and efficiency.