Das VG Wiesbaden verweigerte einem chronisch kranken Referendar mit Konzentrationsschwierigkeiten und erhöhtem Regenerationsbedarf zusätzliche Schreibzeit, da Arbeiten unter Zeitdruck zum Kernbestandteil juristischer Prüfungen gehöre. Diese Schlussfolgerung hält den verfassungsrechtlichen Anforderungen aus Art. 3 Abs. 3 Satz 2 GG („Niemand darf wegen seiner Behinderung benachteiligt werden.“) nicht stand, weil sie bei bestimmten Behinderungen das Recht auf Nachteilsausgleiche, die bei anderen Behinderungen regelmäßig gewährt werden, vorschnell abschneidet.
The paper reviews recent progress in photodetectors, discussing vacuum-based detectors, semiconductor sensors, and gas-based detectors. The emphasis in this review is on the detection of low light levels, enhanced timing resolution, and spectral range of photon detectors, as well as the development of photosensors for extreme conditions, for operation in cryogenic and high radiation-level environments.
According to experts, Simultaneous Localization and Mapping (SLAM) is an intrinsic part of autonomous robotic systems. Several SLAM systems with impressive performance have been invented and used during the last several decades. However, there are still unresolved issues, such as how to deal with moving objects in dynamic situations. Classic SLAM systems depend on the assumption of a static environment, which becomes unworkable in highly dynamic situations. Several methods have been presented to tackle this issue in recent years, but each has its limitations. This research combines the visual SLAM systems ORB-SLAM3 and Detectron2 to present the Det-SLAM system, which employs depth information and semantic segmentation to identify and eradicate dynamic spots to accomplish semantic SLAM for dynamic situations. Evaluation of public TUM datasets indicates that Det-SLAM is more resilient than previous dynamic SLAM systems and can lower the estimated error of camera posture in dynamic indoor scenarios.
Simple Summary Accurate recognition and detection of pests is the basis of integrated pest management (IPM). Manual pest detection is a time-consuming and laborious work. We use computer vision technology to design an automatic aphid detection network. Compared with other methods, our model can improve the performance and efficiency of aphid detection simultaneously. Experimental results prove the effectiveness of our method. Abstract It is well recognized that aphid infestation severely reduces crop yield and further leads to significant economic loss. Therefore, accurately and efficiently detecting aphids is of vital importance in pest management. However, most existing detection methods suffer from unsatisfactory performance without fully considering the aphid characteristics, including tiny size, dense distribution, and multi-viewpoint data quality. In addition, existing clustered tiny-sized pest detection methods improve performance at the cost of time and do not meet the real-time requirements. To address the aforementioned issues, we propose a robust aphid detection method with two customized core designs: a Transformer feature pyramid network (T-FPN) and a multi-resolution training method (MTM). To be specific, the T-FPN is employed to improve the feature extraction capability by a feature-wise Transformer module (FTM) and a channel-wise feature recalibration module (CFRM), while the MTM aims at purifying the performance and lifting the efficiency simultaneously with a coarse-to-fine training pattern. To fully demonstrate the validity of our methods, abundant experiments are conducted on a densely clustered tiny pest dataset. Our method can achieve an average recall of 46.1% and an average precision of 74.2%, which outperforms other state-of-the-art methods, including ATSS, Cascade R-CNN, FCOS, FoveaBox, and CRA-Net. The efficiency comparison shows that our method can achieve the fastest training speed and obtain 0.045 s per image testing time, meeting the real-time detection. In general, our TD-Det can accurately and efficiently detect in-field aphids and lays a solid foundation for automated aphid detection and ranking.
In the paradigm of object detection, the decision head is an important part, which affects detection performance significantly. Yet how to design a high-performance decision head remains to be an open issue. In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection. First, we disentangle the decision choices and prediction values by plugging soft decision trees into neural networks. To facilitate effective learning, we propose randomized decision routing with node selective and associative losses, which can boost the feature representative learning and network decision simultaneously. Second, we develop the decision head for object detection with narrow branches to generate the routing probabilities and masks, for the purpose of obtaining divergent decisions from different nodes. We name this approach as the randomized decision routing for object detection, abbreviated as R(Det)2. Experiments on MS-COCO dataset demonstrate that R(Det)2 is effective to improve the detection performance. Equipped with existing detectors, it achieves 1.4 ~ 3.6% AP improvement.
The detection of arbitrary-oriented and multi-scale objects in satellite optical imagery is an important task in remote sensing and computer vision. Despite significant research efforts, such detection remains largely unsolved due to the diversity of patterns in orientation, scale, aspect ratio, and visual appearance; the dense distribution of objects; and extreme imbalances in categories. In this paper, we propose an adaptive dynamic refined single-stage transformer detector to address the aforementioned challenges, aiming to achieve high recall and speed. Our detector realizes rotated object detection with RetinaNet as the baseline. Firstly, we propose a feature pyramid transformer (FPT) to enhance feature extraction of the rotated object detection framework through a feature interaction mechanism. This is beneficial for the detection of objects with diverse patterns in terms of scale, aspect ratio, visual appearance, and dense distributions. Secondly, we design two special post-processing steps for rotated objects with arbitrary orientations, large aspect ratios and dense distributions. The output features of FPT are fed into post-processing steps. In the first step, it performs the preliminary regression of locations and angle anchors for the refinement step. In the refinement step, it performs adaptive feature refinement first and then gives the final object detection result precisely. The main architecture of the refinement step is dynamic feature refinement (DFR), which is proposed to adaptively adjust the feature map and reconstruct a new feature map for arbitrary-oriented object detection to alleviate the mismatches between rotated bounding boxes and axis-aligned receptive fields. Thirdly, the focus loss is adopted to deal with the category imbalance problem. Experiments on two challenging satellite optical imagery public datasets, DOTA and HRSC2016, demonstrate that the proposed ADT-Det detector achieves a state-of-the-art detection accuracy (79.95% mAP for DOTA and 93.47% mAP for HRSC2016) while running very fast (14.6 fps with a 600 × 600 input image size).
. In this paper we show that for a vector space V d of dimension d there exists a linear map det S 2 : V ⊗ d (2 d − 1) d → k with the property that det S 2 ( ⊗ 1 ≤ i<j ≤ 2 d ( v i,j )) = 0 if there exists 1 ≤ x < y < z ≤ 2 d such that v x,y = v x,z = v y,z . The existence of such a map was conjectured in [4]. We present two applications of the map det S 2 to geometry and combinatorics.
In object detection, the detection backbone consumes more than half of the overall inference cost. Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS). However, existing NAS methods for object detection require hundreds to thousands of GPU hours of searching, making them impractical in fast-paced research and development. In this work, we propose a novel zero-shot NAS method to address this issue. The proposed method, named MAE-DET, automatically designs efficient detection backbones via the Maximum Entropy Principle without training network parameters, reducing the architecture design cost to nearly zero yet delivering the state-of-the-art (SOTA) performance. Under the hood, MAE-DET maximizes the differential entropy of detection backbones, leading to a better feature extractor for object detection under the same computational budgets. After merely one GPU day of fully automatic design, MAE-DET innovates SOTA detection backbones on multiple detection benchmark datasets with little human intervention. Comparing to ResNet-50 backbone, MAE-DET is $+2.0\%$ better in mAP when using the same amount of FLOPs/parameters, and is $1.54$ times faster on NVIDIA V100 at the same mAP. Code and pre-trained models are available at https://github.com/alibaba/lightweight-neuralarchitecture-search.
In this paper we show the existence of a nontrivial linear map $det^{S^3}:V_d^{\otimes\binom{3d}{3}}\to k$ with the property that $det^{S^3}(\otimes_{1\leq i<j<k\leq 3d}(v_{i,j,k}))=0$ if there exists $1\leq x<y<z<t\leq 3d$ such that $v_{x,y,z}=v_{x,y,t}=v_{x,z,t}=v_{y,z,t}$. This gives a partial answer to a conjecture from [10]. As an application, we use the map $det^{S^3}$ to study those d-partitions of the complete hypergraph $K^3_{3d}$ that have zero Betti numbers. We also discuss algebraic and combinatorial properties of a map $det^{S^r}:V_d^{\otimes\binom{rd}{r}}\to k$ which generalizes the determinant map, the map $det^{S^2}$ from [9], and $det^{S^3}$.
Abstract It is widely claimed that native GOx undergoes direct electron transfer (DET) at nanostructured electrodes. In this paper we argue that the vast majority, if not all, of these claims are incorrect. We present results for GOx adsorbed on MWCNTs, a typical nanostructured electrode. We show that the surface redox peaks usually attributed to DET to GOx actually arise from flavin, and possibly catalase, impurities present in the as supplied commercial enzyme that are adsorbed at the electrode surface. We show that the observed response to glucose is due to enzymatic activity, but not electroactivity, of adsorbed GOx that catalyses the reaction of d -glucose with dissolved oxygen leading to a decrease in the oxygen reduction current that correlates with the glucose concentration.
Ship detection is a significant and challenging task in remote sensing. Due to the arbitrary-oriented property and large aspect ratio of ships, most of the existing detectors adopt rotation boxes to represent ships. However, manual-designed rotation anchors are needed in these detectors, which causes multiplied computational cost and inaccurate box regression. To address the abovementioned problems, an anchor-free rotation ship detector, named GRS-Det, is proposed, which mainly consists of a feature extraction network with selective concatenation module (SCM), a rotation Gaussian-Mask model, and a fully convolutional network-based detection module. First, a U-shape network with SCM is used to extract multiscale feature maps. With the help of SCM, the channel unbalance problem between different-level features in feature fusion is solved. Then, a rotation Gaussian-Mask is designed to model the ship based on its geometry characteristics, which aims at solving the mislabeling problem of rotation bounding boxes. Meanwhile, the Gaussian-Mask leverages context information to strengthen the perception of ships. Finally, multiscale feature maps are fed to the detection module for classification and regression of each pixel. Our proposed method, evaluated on ship detection benchmarks, including HRSC2016 and DOTA Ship data sets, achieves state-of-the-art results.
Nikhil Varma Keetha, P. SamsonAnoshBabu, Chandra Sekhara Rao Annavarapu
Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodule in the CT image pose a challenging problem to the robust segmentation of the lung nodules. This article proposes U-Det, a resource-efficient model architecture, which is an end to end deep learning approach to solve the task at hand. It incorporates a Bi-FPN (bidirectional feature network) between the encoder and decoder. Furthermore, it uses Mish activation function and class weights of masks to enhance segmentation efficiency. The proposed model is extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. The U-Det architecture outperforms the existing U-Net model with the Dice similarity coefficient (DSC) of 82.82% and achieves results comparable to human experts.
Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities. However, there are still several obstacles hindering the development. First, ships appear in various scenarios, which makes it difficult to exclude the disruption of the cluttered background. Second, it becomes more complicated to precisely locate the targets with large aspect ratios, arbitrary orientations and dense distributions. Third, the trade-off between accurate localization and improved detection efficiency needs to be considered. To address these issues, this paper presents a rotate refined feature alignment detector (R 2 FA-Det), which ingeniously balances the quality of bounding box prediction and the high speed of the single-stage framework. Specifically, first, we devise a lightweight non-local attention module and embed it into the stem network. The recalibration of features not only strengthens the object-related features yet adequately suppresses the background interference. In addition, both forms of anchors are integrated into our modified anchor mechanism and thus can enable better representation of densely arranged targets with less computation burden. Furthermore, considering the shortcoming of the feature misalignment existing in the cascaded refinement scheme, a feature-guided alignment module which encodes both the position and shape information of current refined anchors into the feature points is adopted. Extensive experimental validations on two SAR ship datasets are performed and the results demonstrate that our algorithm has higher accuracy with faster speed than some state-of-the-art methods.
Lane extraction is a basic yet necessary task for autonomous driving. Although past years have witnessed major advances in lane extraction with deep learning models, they all aim at ordinary RGB images generated by frame-based cameras, which limits their performance in nature. To tackle this problem, we introduce Dynamic Vision Sensor (DVS), a type of event-based sensor to lane extraction task and build a high-resolution DVS dataset for lane extraction (DET). We collect the raw event data and generate 5,424 event-based sensor images with a resolution of 1280x800, the highest one among all DVS datasets available now. These images include complex traffic scenes and various lane types. All images of DET are annotated with multi-class segmentation format. The fully annotated DET images contains 17,103 lane instances, each of which is labeled pixel by pixel manually. We evaluate state-of-the-art lane extraction models on DET to build a benchmark for lane extraction task with event-based sensor images. Experimental results demonstrate that DET is quite challenging for even state-of-the-art lane extraction methods. DET is made publicly available, including the raw event data, accumulated images and labels.
Object detection is a challenging task in aerial images, where many objects have large aspect ratios and are densely arranged. Most anchor-based rotating detectors assign anchors for ground-truth objects by a fixed restriction of the rotation Intersection-over-Unit (IoU) between anchors and objects, which directly follow horizontal detectors. Due to many directional objects with a large aspect ratio, the object-anchor IoU is heavily influenced by the angle, which may cause few anchors assigned for some ground-truth objects. In this study, we propose an anchor selection method based on sample balance assigning anchors adaptively, which we name the Self-Adaptive Anchor Selection (A2S-Det) method. For each ground-truth object, A2S-Det selects a set of candidate anchors by horizontal IoU. Then, an adaptive threshold module is adopted on the set of candidate anchors, which calculates a boundary of these candidate anchors aiming to keep a balance between positive and negative anchors. In addition, we propose a coordinate regression of relative reference (CR3) module to precisely regress the rotating bounding box. We test our method on a public aerial image dataset, and prove better performance than many other one-stage detectors and two-stage detectors, achieving the mAP of 70.64. An efficiency anchor matching method helps the detector achieve better performance for objects with large aspect ratios.
Takumi Yanase, J. Okuda-Shimazaki, Kazushige Mori
et al.
Fungi-derived flavin adenine dinucleotide (FAD)-dependent glucose dehydrogenases (FADGDHs) are the most popular and advanced enzymes for SMBG sensors because of their high substrate specificity toward glucose and oxygen insensitivity. However, this type of FADGDH hardly shows direct electron transfer (DET) ability. In this study, we developed a new DET-type FADGDH by harboring Cytochrome b562 (cyt b562) derived from Escherichia coli as the electron transfer domain. The structural genes encoding fusion enzymes composed of cyt b562 at either the N- or C-terminus of fungal FADGDH, (cyt b562-GDH or GDH-cyt b562), were constructed, recombinantly expressed, and characteristics of the fusion proteins were investigated. Both constructed fusion enzymes were successfully expressed in E. coli, as the soluble and GDH active proteins, showing cyt b562 specific redox properties. Thusconstructed fusion proteins showed internal electron transfer between FAD in FADGDH and fused cyt b562. Consequently, both cyt b562-GDH and GDH-cyt b562 showed DET abilities toward electrode. Interestingly, cyt b562-GDH showed much rapid internal electron transfer and higher DET ability than GDH-cyt b562. Thus, we demonstrated the construction and production of a new DET-type FADGDH using E.coli as the host cells, which is advantageous for future industrial application and further engineering.
We discuss properties of the $det^{S^2}$ map, present a few explicit computations, and give a geometrical interpretation for the condition $det^{S^2}((v_{i,j})_{1\leq i