Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset

Abstract: SMD (Singapore Maritime Dataset) is a public dataset with annotated videos, and it is almost unique in the training of deep neural networks (DNN) for the recognition of maritime objects. However, there are noisy labels and imprecisely located bounding boxes in the ground truth of the SMD. In this paper, for the benchmark of DNN algorithms, we correct the annotations of the SMD dataset and present an improved version, which we coined SMD-Plus. We also propose augmentation techniques designed especially for the SMD-Plus. More specifically, an online transformation of training images via Copy & Paste is applied to solve the class-imbalance problem in the training dataset. Furthermore, the mix-up technique is adopted in addition to the basic augmentation techniques for YOLO-V5. Experimental results show that the detection and classification performance of the modified YOLO-V5 with the SMD-Plus has improved in comparison to the original YOLO-V5. The ground truth of the SMD-Plus and our experimental results are available for download.

Bounding sets of sequential quantum correlations and device-independent randomness certification

Abstract: An important problem in quantum information theory is that of bounding sets of correlations that arise from making local measurements on entangled states of arbitrary dimension. Currently, the best-known method to tackle this problem is the NPA hierarchy; an infinite sequence of semidefinite programs that provides increasingly tighter outer approximations to the desired set of correlations. In this work we consider a more general scenario in which one performs sequences of local measurements on an entangled state of arbitrary dimension. We show that a simple adaptation of the original NPA hierarchy provides an analogous hierarchy for this scenario, with comparable resource requirements and convergence properties. We then use the method to tackle some problems in device-independent quantum information. First, we show how one can robustly certify over 2.3 bits of device-independent local randomness from a two-quibt state using a sequence of measurements, going beyond the theoretical maximum of two bits that can be achieved with non-sequential measurements. Finally, we show tight upper bounds to two previously defined tasks in sequential Bell test scenarios.

Joint-based item recognition

Abstract: For an input image of a person, a set of object proposals are generated in the form of bounding boxes. A pose detector identifies coordinates in the image corresponding to locations on the persons body, such as the waist, head, hands, and feet of the person. A convolutional neural network receives the portions of the input image defined by the bounding boxes and generates a feature vector for each image portion. The feature vectors are input to one or more support vector machine classifiers, which generate an output representing a probability of a match with an item. The distance between the bounding box and a joint associated with the item is used to modify the probability. The modified probabilities for the support vector machine are then compared with a threshold and each other to identify the item.

Single-Stage Rotation-Decoupled Detector for Oriented Object

Abstract: Oriented object detection has received extensive attention in recent years, especially for the task of detecting targets in aerial imagery. Traditional detectors locate objects by horizontal bounding boxes (HBBs), which may cause inaccuracies when detecting objects with arbitrary oriented angles, dense distribution and a large aspect ratio. Oriented bounding boxes (OBBs), which add different rotation angles to the horizontal bounding boxes, can better deal with the above problems. New problems arise with the introduction of oriented bounding boxes for rotation detectors, such as an increase in the number of anchors and the sensitivity of the intersection over union (IoU) to changes of angle. To overcome these shortcomings while taking advantage of the oriented bounding boxes, we propose a novel rotation detector which redesigns the matching strategy between oriented anchors and ground truth boxes. The main idea of the new strategy is to decouple the rotating bounding box into a horizontal bounding box during matching, thereby reducing the instability of the angle to the matching process. Extensive experiments on public remote sensing datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that the proposed approach achieves state-of-the-art detection accuracy with higher efficiency.