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学者姓名：庞俊彪
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Abstract ：
Pavement crack detection is of great significance for road maintenance. However, the complexity of road surfaces and the irregularity of cracks make it difficult to accurately detect crack regions. We propose a crack detection method based on structural features for the patch-wise crack detection. The novelty of this method lies on the fusion of the local patches in a multi-staged strategy. Deep supervision learning is further used to learn these features at each stage. The fusion features model the structural relevance among cracks. The experimental results prove the effectiveness of our method on the dataset collected from the industrial environments. Among these state-of-the-art methods we compared, our model achieved the best experimental results with an AP 86.97%.
Keyword ：
structural feature extraction structural feature extraction deep supervision deep supervision crack detection crack detection
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| GB/T 7714 | Duan, Lijuan , Zeng, Jun , Pang, Junbiao et al. Pavement Crack Detection Using Multi-stage Structural Feature Extraction Model [J]. | 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) , 2021 : 969-973 . |
| MLA | Duan, Lijuan et al. "Pavement Crack Detection Using Multi-stage Structural Feature Extraction Model" . | 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) (2021) : 969-973 . |
| APA | Duan, Lijuan , Zeng, Jun , Pang, Junbiao , Wang, Junzhe . Pavement Crack Detection Using Multi-stage Structural Feature Extraction Model . | 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) , 2021 , 969-973 . |
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Abstract ：
近年来,随着公共交通领域大数据、云计算、移动支付等新兴科技的应用,城市公交、轨道交通等公共交通行业都推出了二维码App实现了"刷手机"乘车.在为用户出行带来便捷的同时,出现了各App平台的信息数据不互联互通和二维码规范不一等问题.这给用户换乘交通工具时带来了不便,同时增加了交通部门的管理成本.本文基于HTTPS(Hyper Text Transfer Protocol over Secure Socket Layer)通信协议、HTML5(HyperText Markup Language 5)通信协议以及MQ(Message Queue)通信协议等多种通信协议构建一码通乘平台,统一各平台的二维码规范和对信息数据,让公共交通资源和数据管理朝着高效化、便捷化、规范化的方向发展.
Keyword ：
Message Queue通信协议 Message Queue通信协议 HTML 5通信协议 HTML 5通信协议 跨平台 跨平台 HTTPS通信协议 HTTPS通信协议
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| GB/T 7714 | 隋莉颖 , 于海涛 , 杜勇 et al. 基于多通信协议的一码通乘平台技术研究 [J]. | 电脑知识与技术 , 2021 , 17 (27) : 10-12 . |
| MLA | 隋莉颖 et al. "基于多通信协议的一码通乘平台技术研究" . | 电脑知识与技术 17 . 27 (2021) : 10-12 . |
| APA | 隋莉颖 , 于海涛 , 杜勇 , 边嘉乐 , 庞俊彪 . 基于多通信协议的一码通乘平台技术研究 . | 电脑知识与技术 , 2021 , 17 (27) , 10-12 . |
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Abstract ：
随着互联网移动支付技术的蓬勃发展,轨道交通、城市公交及远郊公交等交通行业均构建了自己的移动付费乘车平台.但前期多种交通出行运营方付费二维码不通用、数据信息不互通,给交通数据管理、信息反馈和用户出行带来了诸多不便.因此,市民对交通出行付费方式统一化的呼声越来越高.文中基于中心接入规范的交通一码通乘应用方案,提出中心统一发码平台、中心公共管理平台和中心数据交换平台,解决二维码规范不同、用户信息管理不统一、业务规则及管理机制不一致等问题.一码通乘平台的构建可方便市民出行付费,便于交通运营企业的管理,同时为未来城市规划提供更充实的数据支撑.
Keyword ：
城市交通 城市交通 公共交通 公共交通 中心接入规范 中心接入规范 一码通乘 一码通乘
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| GB/T 7714 | 隋莉颖 , 于海涛 , 杜勇 et al. 基于中心接入规范的交通一码通乘 [J]. | 交通科技 , 2021 , (5) : 95-99 . |
| MLA | 隋莉颖 et al. "基于中心接入规范的交通一码通乘" . | 交通科技 5 (2021) : 95-99 . |
| APA | 隋莉颖 , 于海涛 , 杜勇 , 边嘉乐 , 庞俊彪 . 基于中心接入规范的交通一码通乘 . | 交通科技 , 2021 , (5) , 95-99 . |
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Abstract ：
Automatic crack detection from pavement images is an import research field. Meanwhile, crack detection is a challenge task: (1) manual labels are subjective because of low contrast between crack and the surrounding pavement and heavy workload; (2) the excessive dependence of supervised deep learning training on labels. To address these problems, we present an unsupervised method for learning mapping to translate crack images to binary images based on generative adversarial network. We introduce the cyclic consistent loss to increase accuracy of crack localization. Eight residual blocks connected convolutional neural network for feature extraction is used as generator and a 5-layer fully convolutional network is used as discriminator. We analyze the proposed framework and provide qualitative and quantitative comparison. The experimental results show that the proposed method achieves a better performance than several existing methods. © 2020 ACM.
Keyword ：
Binary images Binary images Crack detection Crack detection Convolutional neural networks Convolutional neural networks Convolution Convolution Multimedia signal processing Multimedia signal processing Deep learning Deep learning Pavements Pavements Multimedia systems Multimedia systems
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| GB/T 7714 | Duan, Lijuan , Geng, Huiling , Pang, Junbiao et al. Unsupervised Pixel-level Crack Detection Based on Generative Adversarial Network [C] . 2020 : 6-10 . |
| MLA | Duan, Lijuan et al. "Unsupervised Pixel-level Crack Detection Based on Generative Adversarial Network" . (2020) : 6-10 . |
| APA | Duan, Lijuan , Geng, Huiling , Pang, Junbiao , Zeng, Jun . Unsupervised Pixel-level Crack Detection Based on Generative Adversarial Network . (2020) : 6-10 . |
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Abstract ：
本发明实施例提供一种路面裂缝检测方法、装置、电子设备及介质；该方法包括采集道路的路面图像；对所述路面图像进行预处理，得到分辨率梯度变化的多个输入图像；将所述多个输入图像输入预先训练的路面裂缝检测模型，得到计算结果；其中，所述路面裂缝检测模型是基于样本路面图像和所述样本路面图像的裂缝标记数据训练得到的，所述路面裂缝检测模型包括多个阶段，每个阶段间进行多次多尺度融合；根据所述路面裂缝检测模型的计算结果，输出检测结果。本发明实施例通过输入分辨率梯度变化的多个输入图像，使用具有多尺度融合结构的路面裂缝检测模型，实现了对复杂情况下的路面裂缝检测，减弱了噪声影响，提高了检测精度。
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| GB/T 7714 | 曾君 , 庞俊彪 , 李培育 et al. 路面裂缝检测方法、装置、电子设备及存储介质 : CN202011454642.0[P]. | 2020-12-10 . |
| MLA | 曾君 et al. "路面裂缝检测方法、装置、电子设备及存储介质" : CN202011454642.0. | 2020-12-10 . |
| APA | 曾君 , 庞俊彪 , 李培育 , 段立娟 , 黄庆明 . 路面裂缝检测方法、装置、电子设备及存储介质 : CN202011454642.0. | 2020-12-10 . |
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Abstract ：
本发明提供一种城市交通动态知识图谱的构建方法及装置，方法包括：根据城市交通站点的地点节点以及地点节点属性特征，确定地点节点关系模型；根据预设采样周期获取的地点节点、地点节点属性特征以及地点节点关系模型，构建城市交通动态知识图谱；其中，地点节点属性特征包括：地点节点兴趣点属性特征、地点节点社会事件属性特征、地点节点路链交通属性特征以及地点节点交通属性特征。所述装置用于执行上述方法。本发明提供的城市交通动态知识图谱的构建方法及装置，通过构建城市交通动态知识图谱，能够提高知识图谱的动态特征，更准确的对交通变化进行预测，提高城市交通服务。
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| GB/T 7714 | 庞俊彪 , 王哲焜 , 吕龙龙 et al. 城市交通动态知识图谱的构建方法及装置 : CN202011364436.0[P]. | 2020-11-27 . |
| MLA | 庞俊彪 et al. "城市交通动态知识图谱的构建方法及装置" : CN202011364436.0. | 2020-11-27 . |
| APA | 庞俊彪 , 王哲焜 , 吕龙龙 , 黄庆明 , 尹宝才 . 城市交通动态知识图谱的构建方法及装置 : CN202011364436.0. | 2020-11-27 . |
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Abstract ：
公交到站时间预测作为提高公共交通运输服务水平的重要措施,能够鼓励用户使用公共交通出行,方便调度部门进行合理调度。通过研究现有文献,发现虽然已经提出了很多不同原理的公交到站时间预测方法,但由于各个文献中使用的数据集合、测试规模不同,所以在现有方法之间无法进行有效的比较,从而无法发现公交到站时间预测的基本问题。为了提供可靠准确的数据基础,实现在统一的数据集上公平地比较目前现有的方法,建立了北京市公交到站数据集。该公交到站数据集是目前为止最大的公交运营数据集,其中包含了各种复杂的路况和可能的情况。在北京市公交到站数据集上,通过选择典型到站预测方法,进行实验比较和结果分析,定位出公交到站时间预测的本质...
Keyword ：
公交到站时间预测 公交到站时间预测 GPS数据 GPS数据 算法评估 算法评估 性能比较 性能比较
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| GB/T 7714 | 庞俊彪 , 胡安静 , 黄晶 et al. 基于大规模数据的公交到站时间预测方法比较 [J]. | 计算机技术与发展 , 2019 , 29 (04) : 24-28 . |
| MLA | 庞俊彪 et al. "基于大规模数据的公交到站时间预测方法比较" . | 计算机技术与发展 29 . 04 (2019) : 24-28 . |
| APA | 庞俊彪 , 胡安静 , 黄晶 , 杜勇 , 于海涛 . 基于大规模数据的公交到站时间预测方法比较 . | 计算机技术与发展 , 2019 , 29 (04) , 24-28 . |
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Abstract ：
Organizing webpages into hot topics is one of the key steps to understand the trends from multi-modal web data. To handle this pressing problem, Poisson Deconvolution (PD), a state-of-the-art method, recently is proposed to rank the interestingness of web topics on a similarity graph. Nevertheless, in terms of scalability, PD optimized by expectation-maximization is not sufficiently efficient for a large-scale data set. In this paper, we develop a Stochastic Poisson Deconvolution (SPD) to deal with the large-scale web data sets. Experiments demonstrate the efficacy of the proposed approach in comparison with the state-of-the-art methods on two public data sets and one large-scale synthetic data set. © 2019, Springer Nature Switzerland AG.
Keyword ：
Maximum principle Maximum principle Stochastic systems Stochastic systems
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| GB/T 7714 | Lin, Jinzhong , Pang, Junbiao , Su, Li et al. Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution [C] . 2019 : 590-602 . |
| MLA | Lin, Jinzhong et al. "Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution" . (2019) : 590-602 . |
| APA | Lin, Jinzhong , Pang, Junbiao , Su, Li , Liu, Yugui , Huang, Qingming . Accelerating Topic Detection on Web for a Large-Scale Data Set via Stochastic Poisson Deconvolution . (2019) : 590-602 . |
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Crack width is an important indicator to diagnose the safety of constructions, e.g., asphalt road, concrete bridge. In practice, measuring crack width is a challenge task: (1) the irregular and non-smooth boundary makes the traditional method inefficient; (2) pixel-wise measurement guarantees the accuracy of a system and (3) understanding the damage of constructions from any pre-selected points is a mandatary requirement. To address these problems, we propose a cascade Principal Component Analysis (PCA) to efficiently measure crack width from images. Firstly, the binary crack image is obtained to describe the crack via the off-the-shelf crack detection algorithms. Secondly, given a pre-selected point, PCA is used to find the main axis of a crack. Thirdly, Robust Principal Component Analysis (RPCA) is proposed to compute the main axis of a crack with a irregular boundary. We evaluate the proposed method on a real data set. The experimental results show that the proposed method achieves the state-of-the-art performances in terms of efficiency and effectiveness. © 2018 Association for Computing Machinery.
Keyword ：
Measurement Measurement Principal component analysis Principal component analysis Cascades (fluid mechanics) Cascades (fluid mechanics) Robust control Robust control Binary images Binary images Crack detection Crack detection
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| GB/T 7714 | Duan, Lijuan , Geng, Huiling , Zeng, Jun et al. Fast and accurately measuring crack width via cascade principal component analysis [C] . 2019 . |
| MLA | Duan, Lijuan et al. "Fast and accurately measuring crack width via cascade principal component analysis" . (2019) . |
| APA | Duan, Lijuan , Geng, Huiling , Zeng, Jun , Pang, Junbiao , Huang, Qingming . Fast and accurately measuring crack width via cascade principal component analysis . (2019) . |
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Abstract ：
Bus arrival time prediction intends to improve the level of the services provided by transportation agencies. Intuitively, many stochastic factors affect the predictability of the arrival time, e.g., weather and local events. Moreover, the arrival time prediction for a current station is closely correlated with that of multiple passed stations. Motivated by the observations above, this paper proposes to exploit the long-range dependencies among the multiple time steps for bus arrival prediction via recurrent neural network (RNN). Concretely, RNN with long short-term memory block is used to "correct" the prediction for a station by the correlated multiple passed stations. During the correlation among multiple stations, one-hot coding is introduced to fuse heterogeneous information into a unified vector space. Therefore, the proposed framework leverages the dynamic measurements (i.e., historical trajectory data) and the static observations (i.e., statistics of the infrastructure) for bus arrival time prediction. In order to fairly compare with the state-of-the-art methods, to the best of our knowledge, we have released the largest data set for this task. The experimental results demonstrate the superior performances of our approach on this data set.
Keyword ：
multi-step-ahead prediction multi-step-ahead prediction long-range dependencies long-range dependencies recurrent neural network recurrent neural network heterogenous measurement heterogenous measurement Bus arriving time prediction Bus arriving time prediction
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| GB/T 7714 | Pang, Junbiao , Huang, Jing , Du, Yong et al. Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2019 , 20 (9) : 3283-3293 . |
| MLA | Pang, Junbiao et al. "Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 20 . 9 (2019) : 3283-3293 . |
| APA | Pang, Junbiao , Huang, Jing , Du, Yong , Yu, Haitao , Huang, Qingming , Yin, Baocai . Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2019 , 20 (9) , 3283-3293 . |
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