arXiv subjects

Pengbo Zhao

Publications and source records attributed to Pengbo Zhao.

4 recordsLinked to original sources

arXiv2024

Towards Large Scale Atomic Manufacturing: Heterodyne Grating Interferometer with Zero Dead-Zone

This paper presents a novel heterodyne grating interferometer designed to meet the precise measurement requirements of next-generation lithography systems and large-scale atomic-level manufacturing. Utilizing a dual-frequency light source, the interferometer enables simultaneous measurement of three degrees of freedom. Key advancements include a compact zero Dead-Zone optical path configuration, significantly enhancing measurement reliability by mitigating the impact of light source fluctuations and air refractive index variations. A comprehensive crosstalk error analysis was conducted, resulting in a robust correction algorithm that reduces errors to below 5%. Performance testing of the prototype, size of 90mm*90mm*40mm, demonstrated exceptional resolution (0.25 nm in the XY-axis and 0.3 nm in the Z-axis), superior linearity (6.9e-5, 8.1e-5 and 16.2e-5 for the X, Y, and Z axes, respectively), high repeatability (0.8 nm/1000 nm for the three axes) and stability (20 nm for the XY-axis and 60 nm for the Z-axis over 1000 seconds). Comparative analysis with existing measurement sensors highlights the proposed method's significant advantages in integration, multidimensional capabilities, and is expected to be widely used in fields such as integrated circuits, atomic-level manufacturing and aerospace technology.

physics.optics↗

arXiv2023

Co-visual pattern augmented generative transformer learning for automobile geo-localization

Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aerial (\emph{e.g.}, satellite) images, has received lots of attention but remains extremely challenging due to the drastic appearance differences across aerial-ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules -- one for aerial-aware knowledge generation from ground-view semantics and vice versa -- and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, \emph{i.e.}, {CVACT} and {CVUSA}, demonstrate the effectiveness of the proposed method which sets new records compared with the existing state-of-the-art models.

cs.CV↗

arXiv2020

PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images

Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we represent the oriented objects by polar method in polar coordinate and propose PolarDet, a fast and accurate one-stage object detector based on that representation. Our detector introduces a sub-pixel center semantic structure to further improve classifying veracity. PolarDet achieves nearly all SOTA performance in aerial object detection tasks with faster inference speed. In detail, our approach obtains the SOTA results on DOTA, UCAS-AOD, HRSC with 76.64\% mAP, 97.01\% mAP, and 90.46\% mAP respectively. Most noticeably, our PolarDet gets the best performance and reaches the fastest speed(32fps) at the UCAS-AOD dataset.

cs.CV↗

arXiv2020

Deep Active Learning for Remote Sensing Object Detection

Recently, CNN object detectors have achieved high accuracy on remote sensing images but require huge labor and time costs on annotation. In this paper, we propose a new uncertainty-based active learning which can select images with more information for annotation and detector can still reach high performance with a fraction of the training images. Our method not only analyzes objects' classification uncertainty to find least confident objects but also considers their regression uncertainty to declare outliers. Besides, we bring out two extra weights to overcome two difficulties in remote sensing datasets, class-imbalance and difference in images' objects amount. We experiment our active learning algorithm on DOTA dataset with CenterNet as object detector. We achieve same-level performance as full supervision with only half images. We even override full supervision with 55% images and augmented weights on least confident images.

cs.CV↗