A real-time defect detection in printed circuit boards applying deep learning

Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustnes

Robust direct vision-based pose tracking using normalized mutual information

This paper presents a novel visual tracking approach that combines the NMI metric and the traditional SSD metric within a gradient-based optimization frame, which can be used for direct visual odometry and SLAM. We firstly derivate the closed form expression for first- and second-order analytical NMI derivatives under the assumption of rigid-body transformations, which then can be used by subsequent Newton-like optimization methods. Then we develop a robust tracking scheme that utilizes the robustness of NMI metric while keeping the optimization characteristics of SSD-based Lucas-Kanade (LK) tracking methods. To validate the robustness and accuracy of the proposed approach, several experiments are performed on synthetic datasets as well as real image datasets. The experimental results demonstrate that our approach can provide fast, accurate pose estimation and obtain better tracking performance over standard SSD-based methods in most cases. © 2018 SPIE

Improved Multispectral Skin Detection and its Application to Search Space Reduction for Dismount Detection Based on Histograms of Oriented Gradients

Due to the general shift from conventional warfare to terrorism and urban warfare by enemies of the United States in the late 20th Century, locating and tracking individuals of interest have become critically important. Dismount detection and tracking are vital to provide security and intelligence in both combat and homeland defense scenarios including base defense, combat search and rescue (CSAR), and border patrol. This thesis focuses on exploiting recent advances in skin detection research to reliably detect dismounts in a scene. To this end, a signal-plus-noise model is developed to map modeled skin spectra to the imaging response of an arbitrary sensor, enabling an in-depth exploration of multispectral features as they are encountered in the real world for improved skin detection. Knowledge of skin locations within an image is exploited to cue a robust dismount detection algorithm, significantly improving dismount detection performance and efficiency. This research explores multiple spectral features and detection algorithms to find the best features and algorithms for detecting skin in multispectral visible and short wave infrared (SWIR) imagery. This study concludes that using SWIR imagery for skin detection and color information for false alarm suppression results in 95% probability of skin detection at a false alarm rate of only 0.4%. Skin detections are utilized to cue a dismount detector based on histograms of oriented gradients. This technique reduces the search space by nearly 3 orders of magnitude compared to searching an entire image, while reducing the average number of false positives per image by nearly 2 orders of magnitude at 95% probability of dismount detection. The skin-detection-cued dismount detector developed in this thesis has the potential to make significant contribution to the United States Air Force human measurement and signature intelligence and CSAR missions

Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy

Framework for real-time, autonomous anomaly detection over voluminous time-series geospatial data streams, A

2014 Summer.Includes bibliographical references.In this research work we present an approach encompassing both algorithm and system design to detect anomalies in data streams. Individual observations within these streams are multidimensional, with each dimension corresponding to a feature of interest. We consider time-series geospatial datasets generated by remote and in situ observational devices. Three aspects make this problem particularly challenging: (1) the cumulative volume and rates of data arrivals, (2) anomalies evolve over time, and (3) there are spatio-temporal correlations associated with the data. Therefore, anomaly detections must be accurate and performed in real time. Given the data volumes involved, solutions must minimize user intervention and be amenable to distributed processing to ensure scalability. Our approach achieves accurate, high throughput classications in real time. We rely on Expectation Maximization (EM) to build Gaussian Mixture Models (GMMs) that model the densities of the training data. Rather than one all-encompassing model, our approach involves multiple model instances, each of which is responsible for a particular geographical extent and can also adapt as data evolves. We have incorporated these algorithms into our distributed storage platform, Galileo, and proled their suitability through empirical analysis which demonstrates high throughput (10,000 observations per-second, per-node) and low latency on real-world datasets