S-OHEM: Stratified Online Hard Example Mining for Object Detection

One of the major challenges in object detection is to propose detectors with highly accurate localization of objects. The online sampling of high-loss region proposals (hard examples) uses the multitask loss with equal weight settings across all loss types (e.g, classification and localization, rigid and non-rigid categories) and ignores the influence of different loss distributions throughout the training process, which we find essential to the training efficacy. In this paper, we present the Stratified Online Hard Example Mining (S-OHEM) algorithm for training higher efficiency and accuracy detectors. S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling technique, to choose the training examples according to this influence during hard example mining, and thus enhance the performance of object detectors. We show through systematic experiments that S-OHEM yields an average precision (AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric are 1.6%. Regarding the mean average precision (mAP), a relative increase of 0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based detectors and is capable of acting with post-recognition level regressors.Comment: 9 pages, 3 figures, accepted by CCCV 201

A Review of Object Detection Models based on Convolutional Neural Network

Convolutional Neural Network (CNN) has become the state-of-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.Comment: 17 pages, 11 figures, 1 tabl

Field Evaluation of a Portable Whispering Gallery Mode Accelerometer

An accelerometer utilising the optomechanical coupling between an optical whispering gallery mode (WGM) resonance and the motion of the WGM cavity itself was prototyped and field-tested on a vehicle. We describe the assembly of this portable, battery operated sensor and the field-programmable gate array automation. Pre-trial testing using an electrodynamic shaker demonstrated linear scale-factors with <0.3% standard deviation ( ± 6 g range where g = 9.81 ms - 2 ), and a strong normalised cross-correlation coefficient (NCCC) of r ICP / WGM = 0.997 when compared with an integrated circuit piezoelectric (ICP) accelerometer. A noise density of 40 μ g Hz - 1 / 2 was obtained for frequencies of 2⁻7 kHz, increasing to 130 μ g Hz - 1 / 2 at 200 Hz, and 250 μ g Hz - 1 / 2 at 100 Hz. A reduction in the cross-correlation was found during the trial, r ICP / WGM = 0.36, which we attribute to thermal fluctuations, mounting differences, and the noisy vehicle environment. The deployment of this hand-fabricated sensor, shown to operate and survive during ±60 g shocks, demonstrates important steps towards the development of a chip-scale device

Volumetric Attention for 3D Medical Image Segmentation and Detection

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.Comment: Accepted by MICCAI 201

Comparison of interfacial and electrical characteristics of HfO₂and HfAlO high-k dielectrics on compressively strained Si[sub 1−x]Ge[sub x]

Author name used in this publication: P. F. LeeAuthor name used in this publication: J. Y. Dai2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Contextual Object Detection with a Few Relevant Neighbors

A natural way to improve the detection of objects is to consider the contextual constraints imposed by the detection of additional objects in a given scene. In this work, we exploit the spatial relations between objects in order to improve detection capacity, as well as analyze various properties of the contextual object detection problem. To precisely calculate context-based probabilities of objects, we developed a model that examines the interactions between objects in an exact probabilistic setting, in contrast to previous methods that typically utilize approximations based on pairwise interactions. Such a scheme is facilitated by the realistic assumption that the existence of an object in any given location is influenced by only few informative locations in space. Based on this assumption, we suggest a method for identifying these relevant locations and integrating them into a mostly exact calculation of probability based on their raw detector responses. This scheme is shown to improve detection results and provides unique insights about the process of contextual inference for object detection. We show that it is generally difficult to learn that a particular object reduces the probability of another, and that in cases when the context and detector strongly disagree this learning becomes virtually impossible for the purposes of improving the results of an object detector. Finally, we demonstrate improved detection results through use of our approach as applied to the PASCAL VOC and COCO datasets

Effect of Al addition on the microstructure and electronic structure of HfO₂film

Author name used in this publication: P. F. LeeAuthor name used in this publication: J. Y. Dai2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Revealing microstructural evolutions, mechanical properties and wear performance of wire arc additive manufacturing homogeneous and heterogeneous NiTi alloy

Heterogeneous microstructure designs have attracted a great deal of attention, not only because they have the potential to achieve an ideal combination of two conflicting properties, but also because the processes involved in their fabrication are cost-effective and can be scaled up for industrial production. The process parameters in the preparation process have an important effect on the microstructure and properties of alloy members prepared by wire arc additive manufacturing (WAAM) technology. It was expected that the spatial heterogeneous microstructure with large microstructural heterogeneities in metals can be formed through changing the process parameters. In this work, homogeneous NiTi thin-walled component and heterogeneous NiTi thin-walled component were fabricated using WAAM technology by adjusting the heat input. The effects of deposition height and heat input on the microstructure, mechanical properties and wear properties of WAAM NiTi alloys were investigated. The results show that grains were gradually refined with the increase of deposition height in the homogeneous WAAM NiTi component. The ultimate tensile strength of homogeneous WAAM NiTi component increased from 606.87 MPa to 654.45 MPa and the elongation increased from 12.72% to 15.38%, as the increase of deposition height. Moreover, the homogeneous WAAM NiTi component exhibited excellent wear resistance, the coefficient of friction decreased from 0.760 to 0.715 with the increase of deposition height. Meanwhile, the grains in the heterogeneous WAAM NiTi component shows the finest grains in the central region. The ultimate tensile strength of the lower region, middle region and upper region of heterogeneous WAAM NiTi components were 556.12 MPa, 599.53 MPa and 739.79 MPa, and the elongations were 12.98%, 16.69%, 21.74%, respectively. The coefficient of friction for the lower region, middle region and upper region of heterogeneous WAAM NiTi components were 0.713, 0.720 and 0.710, respectively. The microhardness and cyclic compression properties of the homogeneous components with higher heat input were better than those of the heterogeneous components for the same deposition height. The tensile yield strength, elongation and wear resistance of the heterogeneous components were superior compared to the homogeneous components. These results can be used to optimize the WAAM process parameters to prepare NiTi components with excellent mechanical properties

Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification

Person re-identification (ReID) remains a challenging task in many real-word video analytics and surveillance applications, even though state-of-the-art accuracy has improved considerably with the advent of deep learning (DL) models trained on large image datasets. Given the shift in distributions that typically occurs between video data captured from the source and target domains, and absence of labeled data from the target domain, it is difficult to adapt a DL model for accurate recognition of target data. We argue that for pair-wise matchers that rely on metric learning, e.g., Siamese networks for person ReID, the unsupervised domain adaptation (UDA) objective should consist in aligning pair-wise dissimilarity between domains, rather than aligning feature representations. Moreover, dissimilarity representations are more suitable for designing open-set ReID systems, where identities differ in the source and target domains. In this paper, we propose a novel Dissimilarity-based Maximum Mean Discrepancy (D-MMD) loss for aligning pair-wise distances that can be optimized via gradient descent. From a person ReID perspective, the evaluation of D-MMD loss is straightforward since the tracklet information allows to label a distance vector as being either within-class or between-class. This allows approximating the underlying distribution of target pair-wise distances for D-MMD loss optimization, and accordingly align source and target distance distributions. Empirical results with three challenging benchmark datasets show that the proposed D-MMD loss decreases as source and domain distributions become more similar. Extensive experimental evaluation also indicates that UDA methods that rely on the D-MMD loss can significantly outperform baseline and state-of-the-art UDA methods for person ReID without the common requirement for data augmentation and/or complex networks.Comment: 14 pages (16 pages with references), 7 figures, conference ECC