Vehicle target recognition algorithm for UAV image based on DRFP

针对无人机在复杂战场环境的侦察任务中,目标在视场中尺寸过小、边缘和纹理信息较少所造成的目标识别难题,提出一种新的基于深度学习的单阶段目标识别网络DRFP。DRFP网络以残差结构为骨架,使用特征金字塔结构实现特征融合;其次在损失函数中使用添加了调整因子的交叉熵函数,实现对难样本的重点关注、训练;最后使用高斯型非极大值抑制算法(G-NMS),提高目标密集区检出率。使用无人机航拍图像数据集进行地面车辆目标识别的实验结果表明:所提出的单阶段模型的精度(mAP值)为83.16%,达到了两阶段网络模型的水平;同时,识别速度符合实时性的要求。</p

Dual efficient self-attention network for multi-target detection in aerial imagery

Aerial imagery target detection has been widely used in the military and economic fields. However, it still faces a variety of challenges. In this paper, we proposed several efficiency improvements based on YOLO v3 framework for getting a better small target detection precision. Firstly, a dual self-attention (DAN) block is embedded in Darknet-53's ResNet units to refine the feature map adaptively. Furthermore, the deep semantic features are cascaded with the shallow outline features in a feedforward deconvolutional module to obtain context details of small targets. Finally, introducing online hard examples mining and combining Focal Loss to enhance the discriminating ability between classes. The experimental results on the VEDAI aerial dataset show that the proposed algorithm is significantly improved in accuracy compared to the original network and achieves better performance than two-stage algorithms

A Generic View Planning Algorithm Based on Formal Description of Perception Tasks

We propose a generic view planning algorithm that adjusts postures of the sensor automatically for multi-view perceptions. Formal description of the perception task is brought forward, including objects' prior information library, the perception status, and tasks' completion status. The view planning system operates on the basis of the formal expression, therefore it is not restricted by specific tasks. We employ the features that distinguish all candidate classes, together with features that define the rough shape of each class, as representation of the perceived information about the objects. The existence of features in each candidate class, the description of features, and the location relationship between features are known before perception, and they are filled in the fixed-form prior information library. All status are updated when data is received at a new view. To pick out the view that maximizes the acquisition of effective information, candidate views are sorted by a weighted evaluation function based on the updated status. Experiments of view planning for 3D recognition and reconstruction tasks are conducted, and the result shows that our algorithm has a good performance on multiple tasks

Aripiprazole versus other atypical antipsychotics for schizophrenia

BACKGROUND: In most western industrialised countries, second generation (atypical) antipsychotics are recommended as first line drug treatments for people with schizophrenia. In this review we specifically examine how the efficacy and tolerability of one such agent - aripiprazole - differs from that of other comparable second generation antipsychotics. OBJECTIVES: To evaluate the effects of aripiprazole compared with other atypical antipsychotics for people with schizophrenia and schizophrenia-like psychoses. SEARCH METHODS: We searched the Cochrane Schizophrenia Group Trials Register (November 2011), inspected references of all identified studies for further trials, and contacted relevant pharmaceutical companies, drug approval agencies and authors of trials for additional information. SELECTION CRITERIA: We included all randomised clinical trials (RCTs) comparing aripiprazole (oral) with oral and parenteral forms of amisulpride, clozapine, olanzapine, quetiapine, risperidone, sertindole, ziprasidone or zotepine for people with schizophrenia or schizophrenia-like psychoses. DATA COLLECTION AND ANALYSIS: We extracted data independently. For dichotomous data we calculated risk ratios (RR) and their 95% confidence intervals (CI) on an intention-to-treat basis based on a random-effects model. Where possible, we calculated illustrative comparative risks for primary outcomes. For continuous data, we calculated mean differences (MD), again based on a random-effects model. We assessed risk of bias for each included study. MAIN RESULTS: We included 12 trials involving 6389 patients. Aripiprazole was compared to olanzapine, risperidone and ziprasidone. All trials were sponsored by an interested drug manufacturer. The overall number of participants leaving studies early was 30% to 40%, limiting validity (no differences between groups).When compared with olanzapine no differences were apparent for global state (no clinically important change: n = 703, 1 RCT, RR short-term 1.00 95% CI 0.81 to 1.22; n = 317, 1 RCT, RR medium-term 1.08 95% CI 0.95 to 1.22) but mental state tended to favour olanzapine (n = 1360, 3 RCTs, MD total Positive and Negative Syndrome Scale (PANSS) 4.68 95% CI 2.21 to 7.16). There was no significant difference in extrapyramidal symptoms (n = 529, 2 RCTs, RR 0.99 95% CI 0.62 to 1.59) but fewer in the aripiprazole group had increased cholesterol levels (n = 223, 1 RCT, RR 0.32 95% CI 0.19 to 0.54) or weight gain of 7% or more of total body weight (n = 1095, 3 RCTs, RR 0.39 95% CI 0.28 to 0.54).When compared with risperidone, aripiprazole showed no advantage in terms of global state (n = 384, 2 RCTs, RR no important improvement 1.14 95% CI 0.81 to 1.60) or mental state (n = 372, 2 RCTs, MD total PANSS 1.50 95% CI -2.96 to 5.96).One study compared aripiprazole with ziprasidone (n = 247) and both the groups reported similar change in the global state (n = 247, 1 RCT, MD average change in Clinical Global Impression-Severity (CGI-S) score -0.03 95% CI -0.28 to 0.22) and mental state (n = 247, 1 RCT, MD change PANSS -3.00 95% CI -7.29 to 1.29).When compared with any one of several new generation antipsychotic drugs the aripiprazole group showed improvement in global state in energy (n = 523, 1 RCT, RR 0.69 95% CI 0.56 to 0.84), mood (n = 523, 1 RCT, RR 0.77 95% CI 0.65 to 0.92), negative symptoms (n = 523, 1 RCT, RR 0.82 95% CI 0.68 to 0.99), somnolence (n = 523, 1 RCT, RR 0.80 95% CI 0.69 to 0.93) and weight gain (n = 523, 1 RCT, RR 0.84 95% CI 0.76 to 0.94). Significantly more people given aripiprazole reported symptoms of nausea (n = 2881, 3 RCTs, RR 3.13 95% CI 2.12 to 4.61) but weight gain (7% or more of total body weight) was less common in people allocated aripiprazole (n = 330, 1 RCT, RR 0.35 95% CI 0.19 to 0.64). Aripiprazole may have value in aggression but data are limited. This will be the focus of another review. AUTHORS' CONCLUSIONS: Information on all comparisons are of limited quality, are incomplete and problematic to apply clinically. Aripiprazole is an antipsychotic drug with a variant but not absent adverse effect profile. Long-term data are sparse and there is considerable scope for another update of this review as new data emerges from the many Chinese studies as well as from ongoing larger, independent pragmatic trials

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies