6 research outputs found

    H2RBox-v2: Boosting HBox-supervised Oriented Object Detection via Symmetric Learning

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    With the increasing demand for oriented object detection e.g. in autonomous driving and remote sensing, the oriented annotation has become a labor-intensive work. To make full use of existing horizontally annotated datasets and reduce the annotation cost, a weakly-supervised detector H2RBox for learning the rotated box (RBox) from the horizontal box (HBox) has been proposed and received great attention. This paper presents a new version, H2RBox-v2, to further bridge the gap between HBox-supervised and RBox-supervised oriented object detection. While exploiting axisymmetry via flipping and rotating consistencies is available through our theoretical analysis, H2RBox-v2, using a weakly-supervised branch similar to H2RBox, is embedded with a novel self-supervised branch that learns orientations from the symmetry inherent in the image of objects. Complemented by modules to cope with peripheral issues, e.g. angular periodicity, a stable and effective solution is achieved. To our knowledge, H2RBox-v2 is the first symmetry-supervised paradigm for oriented object detection. Compared to H2RBox, our method is less susceptible to low annotation quality and insufficient training data, which in such cases is expected to give a competitive performance much closer to fully-supervised oriented object detectors. Specifically, the performance comparison between H2RBox-v2 and Rotated FCOS on DOTA-v1.0/1.5/2.0 is 72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%, 89.66% vs. 88.99% on HRSC, and 42.27% vs. 41.25% on FAIR1M.Comment: 13 pages, 4 figures, 7 tables, the source code is available at https://github.com/open-mmlab/mmrotat

    Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization

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    Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection, especially for high-precision detection with high IoU (e.g. 0.75). Instead, we propose to model the rotated objects as Gaussian distributions. A direct advantage is that our new regression loss regarding the distance between two Gaussians e.g. Kullback-Leibler Divergence (KLD), can well align the actual detection performance metric, which is not well addressed in existing methods. Moreover, the two bottlenecks i.e. boundary discontinuity and square-like problem also disappear. We also propose an efficient Gaussian metric-based label assignment strategy to further boost the performance. Interestingly, by analyzing the BBox parameters' gradients under our Gaussian-based KLD loss, we show that these parameters are dynamically updated with interpretable physical meaning, which help explain the effectiveness of our approach, especially for high-precision detection. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation, and experimental results on twelve public datasets (2-D/3-D, aerial/text/face images) with various base detectors show its superiority.Comment: 19 pages, 11 figures, 16 tables, accepted by TPAMI 2022. Journal extension for GWD (ICML'21) and KLD (NeurIPS'21). arXiv admin note: text overlap with arXiv:2101.1195

    The KFIoU Loss for Rotated Object Detection

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    Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics. In contrast, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss instead of the strict value-level identity. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU by its definition, and show its alignment with the SkewIoU at trend-level. This is in contrast to recent Gaussian modeling based rotation detectors e.g. GWD, KLD that involves a human-specified distribution distance metric which requires additional hyperparameter tuning. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU, thanks to its full differentiability and ability to handle the non-overlapping cases. We further extend our technique to the 3-D case which also suffers from the same issues as 2-D detection. Extensive results on various public datasets (2-D/3-D, aerial/text/face images) with different base detectors show the effectiveness of our approach.Comment: 19 pages, 5 figures, 11 tables, tensorflow code: https://github.com/yangxue0827/RotationDetection, pytorch code: https://github.com/open-mmlab/mmrotat

    Dephosphorization in Double Slag Converter Steelmaking Process at Different Temperatures by Industrial Experiments

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    In the present work, the effect of dephosphorization endpoint temperature on the dephosphorization of hot metal was studied for the double slag converter steelmaking process under the conditions of low temperature and low basicity by the industrial experiments. In the temperature range of 1350–1450 °C, with an increasing dephosphorization endpoint temperature, the dephosphorization ratio and phosphorus distribution ratio first increase and then decrease. The phosphorus content in hot metal first decreases and then increases at the end of dephosphorization. At the dephosphorization temperature range of 1385–1410 °C, the dephosphorization ratio is higher than 55%, the P2O5 content in the dephosphorization slag is 3.93–4.17%, the logLP value is 1.76–2.09, the value of PCOP-C of the selective oxidation reaction of carbon and phosphorus is 53–80 Pa, and the aFeO value is 0.284–0.312. The path of phosphorus in hot metal entering the P-rich phase of dephosphorization slag can be reasonably inferred as: hot metal → Fe-rich phase → P-rich phase. Under the present industrial experimental conditions, the dephosphorization and rephosphorization reactions are in dynamic equilibrium at 1413 °C. Considering the experimental results and thermodynamic calculation results of industrial experiments by the double slag dephosphorization process, the optimal temperature range for intermediate deslagging is about 1400–1420 °C

    Dynamic Optics with Transparency and Color Changes under Ambient Conditions

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    Mechanochromic materials have recently received tremendous attention because of their potential applications in humanoid robots, smart windows, strain sensors, anti-counterfeit tags, etc. However, improvements in device design are highly desired for practical implementation in a broader working environment with a high stability. In this article, a novel and robust mechanochromism was designed and fabricated via a facile method. Silica nanoparticles (NPs) that serve as a trigger of color switch were embedded in elastomer to form a bi-layer hybrid film. Upon stretching under ambient conditions, the hybrid film can change color as well as transparency. Furthermore, it demonstrates excellent reversibility and reproducibility and is promising for widespread application
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