Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts)

Randomize to Generalize: Domain Randomization for Runway FOD Detection

Tiny Object Detection is challenging due to small size, low resolution, occlusion, background clutter, lighting conditions and small object-to-image ratio. Further, object detection methodologies often make underlying assumption that both training and testing data remain congruent. However, this presumption often leads to decline in performance when model is applied to out-of-domain(unseen) data. Techniques like synthetic image generation are employed to improve model performance by leveraging variations in input data. Such an approach typically presumes access to 3D-rendered datasets. In contrast, we propose a novel two-stage methodology Synthetic Randomized Image Augmentation (SRIA), carefully devised to enhance generalization capabilities of models encountering 2D datasets, particularly with lower resolution which is more practical in real-world scenarios. The first stage employs a weakly supervised technique to generate pixel-level segmentation masks. Subsequently, the second stage generates a batch-wise synthesis of artificial images, carefully designed with an array of diverse augmentations. The efficacy of proposed technique is illustrated on challenging foreign object debris (FOD) detection. We compare our results with several SOTA models including CenterNet, SSD, YOLOv3, YOLOv4, YOLOv5, and Outer Vit on a publicly available FOD-A dataset. We also construct an out-of-distribution test set encompassing 800 annotated images featuring a corpus of ten common categories. Notably, by harnessing merely 1.81% of objects from source training data and amalgamating with 29 runway background images, we generate 2227 synthetic images. Subsequent model retraining via transfer learning, utilizing enriched dataset generated by domain randomization, demonstrates significant improvement in detection accuracy. We report that detection accuracy improved from an initial 41% to 92% for OOD test set.Comment: 29 pages, 9 figure

Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can recognize such actions when humans perform them during human-robot collaboration tasks. The multi-modal data in this study consists of videos, hand motion data, applied forces as represented by the pressure patterns on the hand, and measurements of the bending of the fingers, collected as human subjects performed manipulation actions. We investigate two different approaches. In the first one, we show that multi-modal signal (motion, finger bending and hand pressure) generated by the action can be decomposed into a set of primitives that can be seen as its building blocks. These primitives are used to define 24 multi-modal primitive features. The primitive features can in turn be used as an abstract representation of the multi-modal signal and employed for action recognition. In the latter approach, the visual features are extracted from the data using a pre-trained image classification deep convolutional neural network. The visual features are subsequently used to train the classifier. We also investigate whether adding data from other modalities produces a statistically significant improvement in the classifier performance. We show that both approaches produce a comparable performance. This implies that image-based methods can successfully recognize human actions during human-robot collaboration. On the other hand, in order to provide training data for the robot so it can learn how to perform object manipulation actions, multi-modal data provides a better alternative

The effect of CSF drain on the optic nerve in idiopathic intracranial hypertension

Background: Elevation of intracranial pressure in idiopathic intracranial hypertension induces an edema of the prelaminar section of the optic nerve (papilledema). Beside the commonly observed optic nerve sheath distention, information on a potential pathology of the retrolaminar section of the optic nerve and the short-term effect of normalization of intracranial pressure on these abnormalities remains scarce. Methods: In this exploratory study 8 patients diagnosed with idiopathic intracranial hypertension underwent a MRI scan (T2 mapping) as well as a diffusion tensor imaging analysis (fractional anisotropy and mean diffusivity). In addition, the clinical presentation of headache and its accompanying symptoms were assessed. Intracranial pressure was then normalized by lumbar puncture and the initial parameters (MRI and clinical features) were re-assessed within 26 h. Results: After normalization of CSF pressure, the morphometric MRI scans of the optic nerve and optic nerve sheath remained unchanged. In the diffusion tensor imaging, the fractional anisotropy value was reduced suggesting a tissue decompression of the optic nerve after lumbar puncture. In line with these finding, headache and most of the accompanying symptoms also improved or remitted within that short time frame. Conclusion: The findings support the hypothesis that the elevation of intracranial pressure induces a microstructural compression of the optic nerve impairing axoplasmic flow and thereby causing the prelaminar papilledema. The microstructural compression of the optic nerve as well as the clinical symptoms improve within hours of normalization of intracranial pressure

Foreign object detection (FOD) using multi-class classifier with single camera vs. distance map with stereo configuration

Detection of objects of interest is a fundamental problem in computer vision. Foreign object detection (FOD) is to detect the objects that are not expected to be appear in certain area. For this task, we need to first detect the position of foreign objects, and then compute the distance to the foreign objects to judge whether the objects are within the dangerous zone or not. The three principle sources of difficulty in performing this task are: a) the huge number of foreign objects categories, b) the calculation of distance using camera(s), and c) the real-time system running performance. Most state-of-art detectors focus on one type or one class of objects. To the best of our knowledge, there is no single solution that focuses on a set of multiple foreign objects detection in an integrated manner. In some cases, multiple detectors can operate simultaneously to detect objects of interest in a given input. This is not efficient. The goal of our research is to focus on detection of a set of objects identified as foreign object in an integrated and efficient manner. We design a multi-class detector. Our approach is to use a coarse-tofine strategy in which we divide the complicated space into finer and finer sub-spaces. For this purpose, data-driven clustering algorithm is implemented to gather similar foreign objects samples, and then an extended vector boosting algorithm is developed to train our multi-class classifier. The purpose of the extended vector boosting algorithm is to separate all foreign objects from background. For the task of estimation of the distance to the foreign objects, we design a look-up table which is based on the area of the detected foreign objects. Furthermore, we design a FOD framework. Our approach is to use stereo matching algorithm to get the disparity information based on intensity images from stereo cameras, and then using the camera model to retrieve the distance information. The distance calculated using disparity is more accurate than using the distance look-up table. We calculate the initial distance map when no objects are in the scene. Block of interest (BOI) is the area where distance is smaller than the corresponding area in the initial distance map. For the purpose of detecting foreign objects, we use flood fill method along with noise suppression method to combine adjacent BOI with higher confidence level.The foreign object detection prototype system has been implemented and evaluated on a number of test sets under real working scenarios. The experimental results show that our algorithm and framework are efficient and robust

Flight deck engine advisor

The focus of this project is on alerting pilots to impending events in such a way as to provide the additional time required for the crew to make critical decisions concerning non-normal operations. The project addresses pilots' need for support in diagnosis and trend monitoring of faults as they affect decisions that must be made within the context of the current flight. Monitoring and diagnostic modules developed under the NASA Faultfinder program were restructured and enhanced using input data from an engine model and real engine fault data. Fault scenarios were prepared to support knowledge base development activities on the MONITAUR and DRAPhyS modules of Faultfinder. An analysis of the information requirements for fault management was included in each scenario. A conceptual framework was developed for systematic evaluation of the impact of context variables on pilot action alternatives as a function of event/fault combinations

Image Analysis Based on Soft Computing and Applied on Space Shuttle During the Liftoff Process

Imaging techniques based on Soft Computing (SC) and developed at Kennedy Space Center (KSC) have been implemented on a variety of prototype applications related to the safety operation of the Space Shuttle during the liftoff process. These SC-based prototype applications include detection and tracking of moving Foreign Objects Debris (FOD) during the Space Shuttle liftoff, visual anomaly detection on slidewires used in the emergency egress system for the Space Shuttle at the laJlIlch pad, and visual detection of distant birds approaching the Space Shuttle launch pad. This SC-based image analysis capability developed at KSC was also used to analyze images acquired during the accident of the Space Shuttle Columbia and estimate the trajectory and velocity of the foam that caused the accident