Horizontal Boxes Research Articles

OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images

Object detection is a fundamental task of remote-sensing image processing. Most existing object detection detectors handle regression and classification tasks through learning from a fixed set of learnable anchors or queries. To simplify object candidates, we propose a denoising diffusion process for remote-sensing image object detection, which directly detects objects from a set of random boxes. During the training phase, the horizontal detection boxes are transformed into oriented detection boxes firstly. Then, the model learns to reverse this transformation process by diffusing from the ground truth-oriented box to a random distribution. During the inference phase, the model incrementally refines a set of randomly generated boxes to produce the final output result. Remarkable results have been achieved using our proposed method. For instance, on commonly used object detection datasets such as DOTA, our approach achieves a mean average precision (mAP) of 76.59%. Similarly, on the HRSC2016 dataset, our method achieves a 72.4% mAP.

Emulating lateral gravity wave propagation in a global chemistry–climate model (EMAC v2.55.2) through horizontal flux redistribution

Abstract. The columnar approach of gravity wave (GW) parameterisations in weather and climate models has been identified as a potential reason for dynamical biases in middle-atmospheric dynamics. For example, GW momentum flux (GWMF) discrepancies between models and observations at 60∘ S arising through the lack of horizontal orographic GW propagation are suspected to cause deficiencies in representing the Antarctic polar vortex. However, due to the decomposition of the model domains onto different computing tasks for parallelisation, communication between horizontal grid boxes is computationally extremely expensive, making horizontal propagation of GWs unfeasible for global chemistry–climate simulations. To overcome this issue, we present a simplified solution to approximate horizontal GW propagation through redistribution of the GWMF at one single altitude by means of tailor-made redistribution maps. To generate the global redistribution maps averaged for each grid box, we use a parameterisation describing orography as a set of mountain ridges with specified location, orientation and height combined with a ray-tracing model describing lateral propagation of so-generated mountain waves. In the global chemistry–climate model (CCM) EMAC (ECHAM MESSy Atmospheric Chemistry), these maps then allow us to redistribute the GW momentum flux horizontally at one level, obtaining an affordable overhead of computing resources. The results of our simulations show GWMF and drag patterns that are horizontally more spread out than with the purely columnar approach; GWs are now also present above the ocean and regions without mountains. In this paper, we provide a detailed description of how the redistribution maps are computed and how the GWMF redistribution is implemented in the CCM. Moreover, an analysis shows why 15 km is the ideal altitude for the redistribution. First results with the redistributed orographic GWMF provide clear evidence that the redistributed GW drag in the Southern Hemisphere has the potential to modify and improve Antarctic polar vortex dynamics, thereby paving the way for enhanced credibility of CCM simulations and projections of polar stratospheric ozone.

Arbitrary-Oriented Detection of Insulators in Thermal Imagery via Rotation Region Network

The precise location of insulators in infrared images is of great significance for insulator condition monitoring and fault diagnosis. Due to the characteristics of insulators themselves and the use of handheld infrared cameras, insulators usually appear in infrared images with different aspect ratios and main axis orientations. Therefore, it is very important and necessary to make full use of the prior knowledge of the insulator itself to accurately locate it. However, most of the existing methods use axial horizontal detection boxes to detect insulators, which cannot take into account the characteristics of the insulator well. When there are large overlapping areas of two horizontal detection boxes, the nonmaximum suppression algorithm may lead to missed detection of the object. To further improve the accuracy of the detection algorithm, this article makes full use of the prior features carried by the insulator itself, and optimizes faster region-based convolutional neural networks (R-CNN) from five aspects: rectangular box representation, feature extraction, candidate box generation, anchor design, and feature alignment. An oriented detection model for infrared images of insulators is constructed. Comparative experiments with a variety of mainstream detection methods were carried out on the constructed infrared dataset. The results show that the proposed method is superior to other models in detection accuracy. When the intersection and union ratio is 0.5, the average precision reaches 95.08%. In addition, it can also effectively predict the shape and angle information of insulators in complex scenes, laying a beneficial foundation for subsequent diagnosis automation tasks.

Anchor-Free Oriented Proposal Generator for Object Detection

Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such as introducing redundant noise, mismatching with ground truths, detracting from the robustness of detectors, etc. In this paper, we propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons horizontal box-related operations from the network architecture. AOPG first produces coarse oriented boxes by a Coarse Location Module (CLM) in an anchor-free manner and then refines them into high-quality oriented proposals. After AOPG, we apply a Fast R-CNN head to produce the final detection results. Furthermore, the shortage of large-scale datasets is also a hindrance to the development of oriented object detection. To alleviate the data insufficiency, we release a new dataset on the basis of our DIOR dataset and name it DIOR-R. Massive experiments demonstrate the effectiveness of AOPG. Particularly, without bells and whistles, we achieve the accuracy of 64.41%, 75.24% and 96.22% mAP on the DIOR-R, DOTA and HRSC2016 datasets respectively. Code and models are available at https://github.com/jbwang1997/AOPG.

SPAN: Strong Scattering Point Aware Network for Ship Detection and Classification in Large-Scale SAR Imagery

Ship detection and classification in synthetic aperture radar (SAR) images play a vital role for wide applications. Due to the unique SAR imaging mechanism, ship detection and classification tasks have faced numerous challenges, such as land interference, image defocus, and noise. Many detectors and classifiers have been presented to handle these problems. However, the general deep learning-based detectors and classifiers lack the combination of SAR characteristics, which leads to poor performance. Compared with optical images, SAR images lack the texture information of ships, which brings great difficulties to the recognition task. To address the above issues, a novel deep learning-based ship detection and classification network combined with scattering characteristics is proposed in this article. First, to accurately locate ships in large-scale SAR images, this article designs a strong scattering point aware network (SPAN) by capturing the strong scattering points that existed in the ship area. SPAN recognizes the ship category according to their distribution characteristics. Second, to compensate for the feature loss caused by the down-sampling operation, this article designs a more suitable resolution recovery module to replace the bilinear interpolation method. Third, a region of interest automatic generation module is proposed to fully utilize the axis-align feature of oriented proposal boxes and the sufficient information of horizontal proposal boxes. Furthermore, the classification encoder module extracts the distribution feature of scattering points to classify SAR ships. Finally, the comprehensive experiments in the large-scale dataset for ship detection and classification in SAR images (LDSD) demonstrate the superior performance of the proposed method.

FAGNet: Multi-Scale Object Detection Method in Remote Sensing Images by Combining MAFPN and GVR

<p indent=0mm>Remote sensing images of large scenes are complex, and have the characteristics of many categories of objects, different scales and changeable directions, which lead to the problem of multi-class, multi-scale and multi-oriented of objects in remote sensing images. A remote sensing image object detection method based on multi-scale attention feature pyramid network (MAFPN) and gliding vertex regression (GVR) mechanism is proposed. Firstly, multi-layer feature maps are extracted from backbone network as input of MAFPN, which combines feature fusion and attention mechanism. On the basis of fusing feature maps of multi-scale, channel attention and spatial attention mechanisms are used to suppress noise, enhance effective feature reuse, and improve the network’s adaptability to object multi-scale features. The fusion feature map output by MAFPN is input to the region proposal network to generate the regions of interest, and then they will be sent to the classification regression network. The GVR mechanism is used in the object classification regression network and the four vertex offset ratio parameters and rotation factors are added on the basis of predicting the horizontal boxes, which converts the horizontal boxes into a rotating box to reduce the redundant area in the bounding boxes, makes the predicted rotating bounding boxes fit the object more closely. The experimental results on the DOTA public dataset, compared with many classical detection algorithms based on convolutional neural networks, show that the average detection accuracy of the proposed method is significantly improved, which can detect objects of multi-scales and multi-oriented more accurately, and achieve the robust detection of multi-scale objects.

Preliminary Development of an Expert System for the Fate Prediction of Marine Radioactivity

Preliminary version of an expert system for the initial prompt assessment of environmental impacts of marine radioactivity materials released routinely and/or accidently has been developed for the technical support of decision makers. The concept of the system is similar to the CORMIX system for the thermal discharge prediction developed by professor Jirka from US. The system uses the multi-compartment model POSEIDON-R equipped with dynamic food chain model so called extended BURN-POSEIDON (Bezhenar et al, 2016) which takes into account the transfer of marine radioactivity through pelagic and benthic marine organisms. In detail, phytoplankton, zooplankton, fish (two types: piscivorous and non-piscivorous), crustaceans, and molluscs are considered for the pelagic food chain, deposit-feeding invertebrates, demersal fish and bottom predators are considered for the benthic food chain and coastal predators feeding by both pelagic and benthic organisms. The modeled region is assumed to have a straight coastline and is divided into a set of horizontal rectangular boxes (or compartments) and vertical layers. Sizes of boxes can vary horizontally but are kept constant. At the outset water depth, currents and mixing without temporal variation are specified in each box. The specified values are constant to the direction parallel to the coastline while they can vary to the direction normal to the coastline. Preliminary experiments have been performed according to a range of hypothetical scenarios where routine and accidental releases of radioactivity in the coastal environment are included. It is expected that the expert system will help to provide decision makers with prompt information about the marine radioactivity contamination level in marine organisms in coastal sea regions where routine and accidental releases of marine radioactivity occur.

Convective turbulent viscosity acting on equilibrium tidal flows: new frequency scaling of the effective viscosity

Turbulent convection is thought to act as an effective viscosity (νE) in damping tidal flows in stars and giant planets. However, the efficiency of this mechanism has long been debated, particularly in the regime of fast tides, when the tidal frequency (ω) exceeds the turnover frequency of the dominant convective eddies (ωc). We present the results of hydrodynamical simulations to study the interaction between tidal flows and convection in a small patch of a convection zone. These simulations build upon our prior work by simulating more turbulent convection in larger horizontal boxes, and here we explore a wider range of parameters. We obtain several new results: (1) νE is frequency dependent, scaling as ω−0.5 when ω/ωc ≲ 1, and appears to attain its maximum constant value only for very small frequencies (ω/ωc ≲ 10−2). This frequency reduction for low-frequency tidal forcing has never been observed previously. (2) The frequency dependence of νE appears to follow the same scaling as the frequency spectrum of the energy (or Reynolds stress) for low and intermediate frequencies. (3) For high frequencies (ω/ωc ≳ 1 − 5), νE ∝ ω−2. 4) The energetically dominant convective modes always appear to contribute the most to νE, rather than the resonant eddies in a Kolmogorov cascade. These results have important implications for tidal dissipation in convection zones of stars and planets, and indicate that the classical tidal theory of the equilibrium tide in stars and giant planets should be revisited. We briefly touch upon the implications for planetary orbital decay around evolving stars.

Convective turbulent viscosity acting on equilibrium tidal flows: new frequency scaling of the effective viscosity

ABSTRACT Turbulent convection is thought to act as an effective viscosity (νE) in damping tidal flows in stars and giant planets. However, the efficiency of this mechanism has long been debated, particularly in the regime of fast tides, when the tidal frequency (ω) exceeds the turnover frequency of the dominant convective eddies (ωc). We present the results of hydrodynamical simulations to study the interaction between tidal flows and convection in a small patch of a convection zone. These simulations build upon our prior work by simulating more turbulent convection in larger horizontal boxes, and here we explore a wider range of parameters. We obtain several new results: (1) νE is frequency dependent, scaling as ω−0.5 when ω/ωc ≲ 1, and appears to attain its maximum constant value only for very small frequencies (ω/ωc ≲ 10−2). This frequency reduction for low-frequency tidal forcing has never been observed previously. (2) The frequency dependence of νE appears to follow the same scaling as the frequency spectrum of the energy (or Reynolds stress) for low and intermediate frequencies. (3) For high frequencies (ω/ωc ≳ 1 − 5), νE ∝ ω−2. 4) The energetically dominant convective modes always appear to contribute the most to νE, rather than the resonant eddies in a Kolmogorov cascade. These results have important implications for tidal dissipation in convection zones of stars and planets, and indicate that the classical tidal theory of the equilibrium tide in stars and giant planets should be revisited. We briefly touch upon the implications for planetary orbital decay around evolving stars.

Effective two-dimensional model for granular matter with phase separation.

Granular systems confined in vertically vibrated shallow horizontal boxes (quasi-two-dimensional geometry) present a liquid-to-solid phase transition when the frequency of the periodic forcing is increased. An effective model, where grains move and collide in two-dimensions is presented, which reproduces the aforementioned phase transition. The key element is that besides the two-dimensional degrees of freedom, each grain has an additional variable ɛ that accounts for the kinetic energy stored in the vertical motion in the real quasi-two-dimensional motion. This energy grows monotonically during free flight, mimicking the energy gained by collisions with the vibrating walls and, at collisions, this energy is instantaneously transferred to the horizontal degrees of freedom. As a result, the average values of ɛ and the kinetic temperature are decreasing functions of the local density, giving rise to an effective pressure that can present van der Waals loops. A kinetic theory approach predicts the conditions that must satisfy the energy growth function to obtain the phase separation, which are verified with molecular dynamics simulations. Notably, the effective equation of state and the critical points computed considering the velocity-time-of-flight correlations differ only slightly from those obtained by simple kinetic theory calculations that neglect those correlations.

Learning Deeply Supervised Scene Text Detectors from Scratch

In this paper, we propose deeply supervised scene text detector (DSTD), a framework that can be learned from scratch. Our proposed method mainly addresses two problems. The first one is that state-of-the-art text detectors rely heavily on the off-the-shelf pre-trained models, which leads to several limitations including inflexibility and domain mismatch. The second problem is that unlike general objects, scene text usually appear in arbitrary orientations. Text detection using horizontal bounding boxes is inaccurate. In DSTD, we propose to regress rotated rectangles directly from horizontal default boxes to deal with multi-oriented text. Furthermore, we abandon the heavy pre-trained model from the SSD framework and incorporate dense layer-wise connections, enabling the network to be learned from scratch. The proposed method is evaluated on two public datasets, namely ICDAR2013 and ICDAR2015. Experimental results demonstrate its superiority over several state-of-the-art approaches.

국화 수확 후 관리기술의 최근 연구 동향

Chrysanthemum is a cut flower species that normally lasts for 1 to 2 weeks, in some cases 3-4 weeks. This has been attributed to low ethylene production during senescence. Reduction in cut flower quality has been attributed to the formation of air embolisms that partially or completely blocks the water transport from the vase solution to the rest of the cut flower stem, increasing hydraulic resistance which may cause severe water stress, yellowing, wilting of leaf, and chlorophyll degradation. Standard type chrysanthemum can be harvested when buds were still tightly closed and then fully opened with the simple bud-opening solution. Standard type chrysanthemum can also be harvested when the minimum size of the inflorescence is about 5-6 cm bud which opened into the first flower full-sized flower. While spray varieties can be harvested when 2-4 most mature flowers have opened (40% opening). Cut flowers are sorted by stem length, weight, condition, and so on. Standard chrysanthemum is 80 cm length for standard type and 70cm for spray type. Pre-treatment with a STS, plant regulator such as GA, BA, 1-MCP, chrysal, germicide, and sucrose, significantly improved the vase life and quality of cut flowers. It is well established that vase solutions containing sugar can improve the vase life of cut chrysanthemum. Chrysanthemum is normally packed in standard horizontal fiberboard boxes. Chrysanthemum should normally be stored at . Precooling resulted in reduction in respiration, decomposition, and transpiration activities as well as decoloration retardation. There was significant difference between wet storage in 3 weeks and dry storage in 2 weeks. In separate pulsing solution trials, various germicides were tested, as well as PGRs to maintain the green color of leaves and turgidity. Prolonging vase life was attained with the application of optimal solution such as HQS, , GA, BA and sucrose. This also retarded senescence in leaves of cut flower stems. Fresh cut chrysanthemum can be transported using a refrigerated van with . Increasing consumption and usage of cut chrysanthemum of various cultivars would require efficient transport system, and effective information exchange among producer, wholesaler, and consumer.

Implementation of Subgrid Cloud Vertical Structure inside a GCM and Its Effect on the Radiation Budget

Abstract The GISS (Goddard Institute for Space Studies) GCM (general circulation model) predicts stratiform and convective cloud cover and optical thickness at nine atmospheric levels in horizontal grid boxes of 4° lat × 5° long. Until now, the radiative fluxes were calculated once per grid box, assuming clear sky or a complete cloud cover. Here, a refinement of the radiative flux calculation is explored by introducing a horizontal subgrid cloud overlap scheme in which cloud blocks are formed by adjacent cloud layers using maximum overlap. Different cloud blocks are separated by an atmospheric level of clear sky and are assumed to overlap randomly inside the grid box. This subgrid cloud structure allows determination of the occurrence probabilities of columns with different vertical structures inside each horizontal grid box. Then, radiative fluxes are calculated for each of these columns. The radiative fluxes of each horizontal grid box are obtained as the occurrence probability weighted sum of the colum...

STEADY STATE AND TIME EVOLUTION WITH PARTICULAR REFERENCE TO HEAT TRANSFER SYSTEMS (STEADY STATE AND TIME EVOLUTION)

Abstract The conditions of validity of the “theorem of minimum entropy production at the steady state” in systems with constant boundary conditions are practically never verified; furthermore some particular cases, near to equilibrium at will, have the maximum entropy production rate just at the steady state, as can be strictly demonstrated. The aim of this paper is to consider the criteria for the steady state, with particular reference to heat conduction processes, in systems with constant boundary temperatures, on the basis of a critical review of the literature, of theoretical considerations regarding chiefly one-dimensional heat conduction, and of experimental studies on Rayleigh-Benard convection. Various maximum and minimum principles are examined in order to understand their validity and limits better, also with reference to the results of laboratory experiments carried out on horizontal square boxes filled with a high Prandtl number fluid heated from betow and cooled on the top. Besides, some results of experiments reported by other authors, but in which the present author was interested, are considered in reference to the analysis of this paper.

Oxygen Depletion Modelling, Port Shelter, Hong Kong

In order to describe and evaluate the effect on the oxygen concentrations of a planned sewage outlet in the Port Shelter Bay in Hong Kong, a relatively simple BOD-DO-box model has been established. The model describes the oxygen concentration in 6 horizontal boxes in the central part of Port Shelter. Each box is vertically divided into 4 layers (sub-boxes). The oxygen concentration in each layer is described as a function of the vertical and the horizontal mixing, the oxygen demand of the sediment and the concentration of organic matter in the water. A finite difference transport dispersion model provided input data for the BOD-DO model. Two main calculations have been made: one simulating the cold season (15°C) and one simulating the warm season (30°C). Furthermore sensitivity analyses have been carried out. The calculations show an oxygen depletion of approximately 1 and 2 mg O2/l at 15° and 30°C respectively in the bottom layer over an area of approximately two sq. km. The highest oxygen depletion is calculated in the bottom layer. From the calculation it can further be concluded that the decay rate of organic matter in the water and the vertical mixing will influence the oxygen depletion considerably. In periods where either the decay rate is higher than 3 d−1, or the vertical mixing is considerably low an oxygen depletion higher than the calculated can be expected. The lowest measured oxygen concentration in Port Shelter during the period July-August 1982 was 1.7 mg O2/l. Therefore it can be expected that the planned outlet will cause oxygen-free or nearly oxygen-free conditions in the bottom layer in warm periods with low vertical mixing.