WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

食品安全国家标准中维生素的测定方法及进展

人体所必需的营养素除了蛋白质、脂类、糖类等能量物质外,维生素也是不可或缺的营养物质,是评价其营养质量的关键指标。检测食品中的维生素含量,是有效保证其质量与安全的必要条件。因此,对现有国家标准食品中维生素的检测方法进行综述,总结各方法的前处理及检出限等,为增强检测工作效率,以及食品检测方法的研究和发展提供参考。     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

工艺参数对动态流量控制法挤压弯管应力的影响

采用有限元模拟和实验研究了挤压钛合金弯曲管件。通过实验验证了工件的形状和尺寸精度,并通过有限元模拟分析了工艺参数对挤出过程中变形体的平均压应力分布情况和挤出弯管件的曲率半径的影响规律。结果表明:有限元模拟中,弯管件的曲率半径误差为6.03%,弯管直径误差为3.82%;在靠近定径带处,平均压应力呈非均匀分布;在焊合腔内,靠近细分流孔区域的平均压应力小于靠近粗分流孔区域的平均压应力,平均压应力的大小顺序在通过粗、细分流孔前后相反;在模具结构固定不变时,弯管件的曲率半径随挤压速度的减小而增大,不随挤压温度的变化而变化。     

梯度铝蜂窝夹芯板的力学行为

梯度分层铝合金蜂窝板是一种有效的吸能结构,本工作在梯度铝蜂窝结构的基础上根据梯度率的概念,通过改变蜂窝芯层的胞壁长度,设计了4种质量相同、梯度率不同的铝蜂窝夹芯结构。通过准静态压缩实验,并结合非线性有限元模拟准静态及冲击态下梯度铝蜂窝夹芯结构的变形情况及其力学性能,分析对比了相同质量下梯度铝蜂窝夹芯结构在准静态下的变形模式以及冲击载荷下分层均质蜂窝结构和不同梯度率的分层梯度蜂窝结构的动态响应和能量吸收特性。结果表明:在准静态压缩过程中,铝蜂窝梯度夹芯板的变形具有明显的局部化特征,蜂窝芯的变形为低密度优先变形直至密实,层级之间的密实化应变差随芯层密度的增大而逐渐减小;在高速冲击下,梯度蜂窝板并非严格按照准静态过程中逐级变形直至密实,而是在锤头冲击惯性及芯层密度的相互作用下整体发生的线弹性变形、弹性屈曲、塑性坍塌及密实化;另外,在本工作所设计的梯度率中,当梯度率为γ₁=0.0276时,梯度蜂窝夹芯板的吸能性达到最好,相较于同等质量下的均质蜂窝夹芯板,能量吸收提高了10.63%。     

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.     

基于光刀图像的机器人示教纠偏方法

为了解决机器人辅助在线检测系统示教过程中效率低、人为干预多的问题,针对孔类特征,提出一种通过处理光刀图像获取机器人位姿纠正参数的方法。在该方法中,纠偏过程按照将图像光刀线调整为水平方向、将被测特征调整至图像中央、根据不同特征调整最优扫描方向3个步骤进行。根据光刀图像可以求出测量坐标系与被测特征的位姿关系,并得到该位姿与理想测量位姿的偏差。经过坐标变换即可得到在机器人基坐标系下的调整参数。另外,可利用电脑控制机器人运动,从而实现了纠偏过程的自动化。实验结果表明,该方法可将初始的示教位姿修正为理想位姿,且比传统方式效率更高。     

Embedding carbon dots in Eu3+-doped metal-organic framework for label-free ratiometric fluorescence detection of Fe3+ ions

Fluorescent detection is a new spectroscopic measurement for ions sensing due to the advantages of real-time determination with high selectivity, accuracy, and low cost. However, chemosensors based on fluorescent detection are usually determined by absolute intensity from a monochromatic emission signal, which is easy to be fluctuated by the external environment, especially for Fe³⁺ detection in complex fluids. Herein, we rationally design a dual-emission Eu³⁺: CDs@ZIF-8 to construct a ratiometric fluorescent sensor with self-calibrating ability for Fe³⁺ determination. High efficient carbon dots (CDs) are embedded in europium ions (Eu³⁺)-doped MOF by simple stirring preparation at room temperature. The label-free ratiometric fluorescent probe (I_CDs@ZIF-8/I_Eu) exhibits simultaneous blue and red emission under the same excitation at 365 nm. Remarkably, Eu³⁺: CDs@ZIF-8 displays the superiority of high selectivity to Fe³⁺, which shows ratiometric fluorescence characteristics (I₀/I) in a range of 0-6 μmol\L with a low limit of detection (LOD) of 0.897 μmol\L. Besides, the CDs-MOF nanocomposite holds good aqueous dispersibility and low cytotoxicity, which shows great potential applications in medical aid including biological detection and clinical diagnosis.     

Droplet Entrainment Analysis in a Flash Evaporator with an Image-Based Measurement Technique

Local droplet sizes and volumes of entrained liquid are captured with an image-based measurement technique for comparison with a conventional, integral method for entrainment analysis. Experiments in a forced circulation flash evaporation were performed for different operating conditions and with two different chemical systems. Droplet size and frequency rise with an increase in thermal energy input. The local readings confirm the trends found by the integral measurement method. The modification of the image-based probe enables the detection of small (≈ 10 µm) and at the same time fast droplets under challenging operating conditions, such as vacuum and superheated feed similar to industrial process conditions.