YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

A comprehensive survey on computer vision based concepts,methodologies, analysis and applications for automatic gun/knife detection

The ability to detect gun and gun held in hand or other body parts is a typical human skill. The same problem presents an imperative task for computer vision system. Automatic observer independent detection of hand held gun or gun held in the other body part, whether it is visible or concealed, provides enhance security in vulnerable places and initiates appropriate action there. Compare to the automatic object detection systems, automatic detection of gun has very few successful attempts. In the present scope of this paper, we present an extensive survey on automatic detection of gun and define a taxonomy for this particular detection system. We also describe the inherent difficulties related with this problem. In this survey of published papers, we examine different approaches used in state-of-the-art attempts and compare performances of these approaches. Finally, this paper concludes pointing to the possible research gaps in related fields.     

Organic mesh template-based laminated object manufacturing to fabricate ceramics with regular micron scaled pore structures

A new aqueous slurry-based laminated object manufacturing process for porous ceramics is proposed: firstly, an organic mesh sheet is pre-paved as a pore-forming template before slurry layer scraping; secondly, the 2D pattern is built with laser outline cutting of the dried mesh–ceramic composite layer; finally, the pore structure is formed after degreasing and sintering. Alumina parts with porosities of 51.5 %, round hole diameters of 80 ± 5 μm were fabricated using 70 wt. % solid content slurry and 100 mesh nylon net. Using an organic mesh as the framework and template not only reduces the risk of damage of the green body but also ensures the regularity, uniformity and connectivity of the micron scaled pore network. The layer-by-layer drying method avoids the delamination phenomenon and improves the paving density. The new method can realize the flexible design of the pore structure by using various organic mesh templates.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Application of the Microsoft Excel Solver tool in the solution of optimization problems of heat exchanger network systems

Optimization of heat exchanger networks (HEN_S) is a field of interest in Chemical Engineering. The aim is to use the residual heat of process streams to raise the temperature of other process streams which are cold, thus minimizing the use of hot and cold utilities. The usual way to deal with this task is to pose a linear programming problem according to a transshipment model in order to minimize either the general services consumptions or the number of hot and cold stream matchings required. This optimization is subjected to several restrictions given by the energy balances corresponding to each heat exchange.The high number of variables and equations obliges the use of specific software to solve these problems, e.g. GAMS (General Algebraic Modelling System). However, this software is not intuitive and requires a long time of training and a certain level of practice for the students to adequately use it.This work describes the use of Microsoft Excel-Solver for the sequential solving of HEN_S optimization problems according to the transshipment model. Solver is a much simpler, intuitive and easily available tool for the students who, besides, usually learn how to use it in the first years of their degree, in Informatics or similar subjects. Thus, it is not necessary to spend extra time in the teaching of the software, time that may be devoted to other contents.This paper presents an example of HEN_S problem proposed in the subject Simulation and Optimization of Chemical Processes corresponding to the Joint Master in Chemical Engineering of the Rey Juan Carlos University and the Autónoma University of Madrid and solved by the students using Excel-Solver.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

网络安全等级保护测评中测评结论的度量方法优化

文章分析了网络安全等级保护2.0时期国家标准的新变化对等级测评结论可能产生的影响,并用实际案例和数据论述了以往描述的基于测评指标和基于测评对象的定量分析方法存在的局限性。根据网络安全等级保护国家标准结构和内容的新特点,结合新的等级测评结论表述方法,文章提出了调整和优化定量计算产生等级测评结论的思路,给出了缺陷扣分的原理和缺陷扣分的定量计算方法,并比较了各种定量计算方法在计算结果上的差异,提出了适合新标准的测评结论定量计算公式。     

VFP环境下报表设计研究

Visual FoxPro是小型数据库应用系统开发工具,而报表设计是数据库应用系统开发的一个重要组成部分,直接决定系统的输出是否能够满足用户的需要。为此详细研究了报表设计方法,结合Microsoft的OLE技术,引入面向对象方法,提出了一种新的报表设计方法。实践应用证明,该方法非常适合设计复杂的报表。     

基于Internet的远程教育系统模型研究

随着计算机网络技术的发展，远程教学系统的结构发生了重大的变化，逐步由原来的客户机／服务器(C／S)结构转变为基于Internet的浏览器／服务器(B／S)结构。本文提出了基于CORBA技术的B／S结构系统模型。分析了其运行过程，并与传统的结构进行了比较，指明了其优越性。在本文的结尾。指出了使用这种技术的一个具体例子。