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Sheet metal stamping is widely used for high-volume production. Despite the wide adoption, it can lead to defects in the manufactured components, making their quality unacceptable. Because of the variety of defects that can occur on the final product, human inspectors are frequently employed to detect them. However, they can be unreliable and costly, particularly at speeds that match the stamping rate. In this paper, we propose an automatic inspection framework for the stamping process that is based on computer vision and deep learning techniques. The low cost, remote sensing capability and simple implementation mean that it can be easily deployed in an industrial setting. A particular focus of this research is to account for the harsh lighting conditions and the highly reflective nature of products found in manufacturing environments that affect optical sensing techniques by making it difficult to capture the details of a scene. High dynamic range images can capture details of an environment in harsh lighting conditions, and in the context of this work, can capture highly reflective metals found in sheet metal stamping manufacturing. Building on this imaging technique, we propose a framework including a deep learning model to detect defects in sheet metal stamping parts. To test the framework, sheet metal ‘Nakajima’ samples were pressed with an industrial stamping press. Then optimally exposed, sequence of exposures, tone-mapped and high dynamic range images of the samples were used to train convolutional neural network-based detectors. Analysis of the resulting models showed that high dynamic range image-based models achieved substantially higher accuracy and minimal false-positive predictions.

Investigating features from gait biometric is an essential step for solving human identification problems, medical diagnosis, monitoring and rehabilitation. This chapter addresses the use of a low-cost Kinect V2.0 sensor along with hand-crafted and deep learning techniques for the investigation. As deep neural network is a representation of the most effective machine learning technology in biomedical domain, its usage in this work is extremely significant to determine the accuracy, reliability and feasibility of observational assessments of spatio-temporal features of gait. A novel approach for human detection and tracking is proposed in this chapter, which involves gait feature learning principles from Color Depth videos. A proposed semi-dynamic object tracking algorithm is used to extract the human object. Gait energy image (GEI) representation is exemplified to train a 2D Convolutional Neural Network for automatic feature extraction in one case. In another case, kinematic hip joint angles are extracted via the stick model generated using body aspect ratios. A maximum accuracy of 95.1% is attained from the CNN models, which infers that the gait features automatically extracted are informative enough. Reliability analysis of all features is carried out using statistical methods such as hypothesis testing (T-test as sample size > 30), Kolmogorov-Smirnov (KS) test, Pearson’s r test and energy distance test to ensure feature learning for biometric applications. Almost all the outcomes illustrate that the features come from the same population mean (as critical region value of 0.05 is crossed) and hence are promising for assessing the strategies that impact the inter-record contrasts among motion signatures.

Investigating new features for human cognitive state classification is an intiguing area of research with Electroencephalography (EEG) based signal analysis. We plan to develop a cost-effective system for cognitive state classification using ambulatory EEG signals. A novel event driven environment is created using external stimuli for capturing EEG data using a 14-channel Emotiv neuro-headset. A new feature extraction method, Gammatone Cepstrum Coefficients (GTCC) is introduced for ambulatory EEG signal analysis. The efficacy of this technique is compared with other feature extraction methods such as Discrete Wavelet Transformation (DWT) and Mel−Frequency Cepstral Coefficients (MFCC) using statistical metrics such as Fisher Discriminant Ratio (FDR) and Logistic Regression (LR). We obtain higher values for GTCC features, demonstrating its discriminative power during classification. A superior performance is achieved for the EEG dataset with a novel ensemble feature space comprising of GTCC and MFCC. Furthermore, the ensemble feature sets are passed through a proposed 1D Convolution Neural Networks (CNN) model to extract novel features. Various classification models like Probabilistic neural network (P-NN), Linear Discriminant Analysis (LDA), Multi-Class Support Vector Machine (MCSVM), Decision Tree (DT), Random Forest (RF) and Deep Convolutional Generative Adversarial Network (DCGAN) are employed to observe best accuracy on extracted features. The proposed GTCC, (GTCC+MFCC) & (GTCC +MFCC +CNN) features outperform the state-of-the-art techniques for all cases in our work. With GTCC+MFCC feature space and GTCC+MFCC+CNN features, accuracies of 96.42% and 96.14% are attained with the DCGAN classifier. Higher classification accuracies of the proposed system makes it a cynosure in the field of cognitive science.

Human mental state classification using Electroen-cephalogram(EEG) signal is one of the dynamic investigating areas in developing intelligent machine frameworks. In this paper, we propose an ambulatory cognitive state classification system using statistical feature analysis. The aim is to develop a realtime system which captures human cognitive states through computer-mediated help based on physiological signals. The walking cognitive state estimator is developed using a realtime Augmented Cognition (AugCog) framework. Six different normality testing methods i.e. Kolmogorov-Smirnov, Anderson Darling, Cramer-Von Mises, Shapiro Wilk, Jarque-Bera, D'Ago-stino and Pearson's test are applied to achieve better accuracy. Box - Cox - transformation is also applied to ensure whether the data follows a normal distribution. We apply Fisher Discriminant Ratio (FDR) for selecting the most discriminative feature amongst all the statistical features (i.e. Mean, Median, Mode, Standard Deviation, Variance, Skewness and Kurtosis) obtained from the signal. Various machine learning methods are applied on the extracted features for training, testing and validation. Probabilistic neural network (P-NN) with Kurtosis feature is found to produces a maximum accuracy of 92.61 % as compared to other classifiers.

Human pose estimation in a gait sequence is an essential step for solving human identification problems, medical diagnosis, monitoring, and rehabilitation. In this paper, a low-cost Kinect V2.0 sensor is used for investigating motion signatures obtained from normal healthy adults. The purpose of this study is to determine the accuracy and reliability of observational assessments of spatio-temporal features. A novel approach for human detection and tracking is proposed, which involves gait feature learning principles from depth and RGB video. In the first step, a human object from the depth image is extracted using the proposed semi-dynamic object tracking algorithm, and a stick model is generated using body aspect ratios to extract hip angles. In the second step, the gait energy image (GEI) representation is utilized for training a 2D Convolutional Neural Network (AlexNet) for automatic feature extraction. A key point detection algorithm is proposed for estimating knee, hip, and ankle joints from RGB gait videos. The reliability analysis of motion signatures is performed using various statistical methods to ensure feature learning for multi-scenario applications. The statistical results are promising for evaluating the methods which influence the inter-record differences among motion signatures.

IDDRG - Conference Proceedings - Tools and Technologies for the Processing of ultra high strength steels