Characterization of surface deformation with the Edge of LightTM technique

The aircraft lap joints are inspected with an enhanced visual inspection technique named “Edge of Light”, which is patented by the NRC Institute for Aerospace Research. This technique is applicable for rapid detection of possible hidden corrosion in lap joints. The surface deformation due to hidden corrosion can be characterized by this optical-based inspection method. In this study, a calibration procedure is developed to quantify the lap joint surface deformation. The effect of surface reflectivity is investigated with the solid film highlighting technique (SolidHi^TM), which helps achieve a uniform reflectivity during the inspection. The efficiency of the technique is demonstrated with the experimental results. This work is supported by NRC-IAR New Initiative Research Funding.     

基于视觉检测技术的浮球姿态测量

给出一种基于图像信息估计被测浮球及其中心轴孔空间姿态的视觉检测方法.若相机焦距已知,且给定空间球与圆特征的形状参数,则可由单视方法估计该球心与圆特征中心位置及其法向方向.基于该算法,可实现对被测球体中心轴端面法线方向的估计,同时还可估计出被测球体及端面中心的位置,从而实现对目标浮球位置及姿态的非接触测量.数值仿真及实际图像处理结果表明了该算法的有效性.     

计算机视觉检测技术在吻合器检测中的应用研究

提出了一种利用计算视觉检测带手柄吻合器漏针新方法。通过采用正面光源投照的检测装置,综合运用图像处理和识别方法对利用CMOS相机采集的吻合器图像进行数学形态学,图像增强等方法处理,并提出模糊局部阈值分割算法实现吻合器漏针的自动识别,并根据面积特征判别漏针数。实验表明：该方法能够对多种型号颜色的吻合器进行稳定、准确的快速检测,检测正确率可达95.6％,平均每次检测时间为2.385s,本方法实用性强,适合现代化生产的要求。     

基于GMR传感器的电涡流检测系统关键技术研究

设计了一种基于巨磁电阻（GMR）的新型电涡流探头及其检测实验系统,重点阐述了系统的工作原理、系统各部分所采用的关键技术及设计中需注意的问题,并对多层铝板试件进行了检测实验研究。实验结果表明：设计的GMR电涡流探头在多层导电结构深层缺陷检测时,与相应的线圈式探头相比,具有更高的灵敏度和更强的深层缺陷检测能力。     

电力巡检移动操作机控制系统的分析与设计

为了提高电力巡检移动机器人的实时性和可操作性,介绍一种以嵌入式Linux系统为核心的电力巡检移动操作机控制系统的软硬件体系结构。给出移动操作臂的动态模型,并基于D-H方法和公式法,给出基于该体系结构的具有冗余自由度电力巡检移动操作机逆运动学分析。在实验中,利用固定在移动机器人上的CCD摄像机对目标物体进行定位,然后根据目标物体的位姿信息,通过逆运动学分析的结果控制移动操作臂实现了对目标物体的抓取操作。进一步分析说明了所设计的体系结构的合理性和可行性。     

基于最优形态学滤波的面料缝纫加工检测

为了对服装加工质量进行自动检测,提出了一种对针织面料缝纫加工的疵点进行自动检验的方法。该方法首先基于遗传算法进行结构元素的寻优,然后通过图像的数学形态学滤波处理来消除面料组织的正常纹理对图像分割的干扰;最后利用灰度阈值实现图像分割来得到表现缝线和加工疵点特征的二值图像。实验结果表明,该方法是有效的。     

曲面工件超声检测中碰撞干涉的研究

在超声检测自动探伤过程中,探头可能和工件发生碰撞干涉。根据曲面工件和圆柱形探头碰撞干涉的特点,给出了一种分级碰撞干涉检测方法。首先利用包围盒算法和几何求交算法剔除大量被检对象分离的情况,然后将可能干涉的3维对象正投影到2维平面中,空间碰撞问题就转化为平面碰撞问题,只需计算投影图是否有重叠就可精确判断是否干涉。工程应用表明,该方法能满足超声检测机器人碰撞干涉检测的高效性和精确性要求,该方法也适用于装配和数控加工中复杂曲面和圆柱体之间的碰撞干涉快速检查。     

基于 FEF-DeepLabV3+的电力金具锈蚀分割方法

金具锈蚀在输电线路航拍图像中细节丰富且分布不规律,为克服分割检测过程中局部信息丢失、精度低和速度慢等问题,提出基于DeepLabV3+的金具锈蚀语义分割模型。替换其主干网络为轻量化改进MobileNetV3网络加快运算速度,并提出自适应特征金字塔(adaptive feature pyramid, AFP)结构融合多尺度。结合FRN层提出特征融合空洞空间金字塔池化(feature fusion atrous spatial pyramid pooling, FEF-ASPP)结构,能够在加强像素间联系的同时不降低分辨率。最后优化损失函数,提高算子的有效性。实验表明,mIoU和mPA分别达到了87.15%、96.64%,相较于原模型提高了3.09%、4.29%。参数量仅为原模型的48%,推理时间仅为15.94 ms,降低了对设备算力的要求,实现高效高精度、轻量化的输电设备锈蚀缺陷分割检测。     

基于改进YOLOv5的输电线路鸟巢检测方法研究

输电线路上的鸟巢会对电力设备的安全运行构成威胁,甚至影响整个电力系统的稳定性。针对复杂场景下输电线路鸟巢检测方法适用性较差的问题,提出一种基于改进YOLOv5的输电线路鸟巢检测方法。该方法结合通道注意机制和空间注意机制设计特征平衡网络,以通道权值和空间权值作为引导,实现检测网络不同层次特征之间语义信息和空间信息的平衡。同时,为了避免因网络层数增加导致特征信息不断被弱化的问题,设计特征增强模块以捕获与鸟巢相关的通道关系和位置信息。最后,利用输电线路无人机巡检图像建立鸟巢数据集进行训练和测试。实验结果表明,所提出的输电线路鸟巢检测方法具有较强的泛化能力和适用性,同时也为电力图像缺陷检测提供技术参考。     

AR-based deep learning for real-time inspection of cable brackets in aircraft

In the process of aircraft assembly, there exist numerous and ubiquitous cable brackets that shall be installed on frames and subsequently need to be manually verified with CAD models. Such a task is usually performed by special operators, hence is time-consuming, labor-intensive, and error-prone. In order to save the inspection time and increase the reliability of results, many researchers attempt to develop intelligent inspection systems using robotic, AR, or AI technologies. However, there is no comprehensive method to achieve enough portability, intelligence, efficiency, and accuracy while providing intuitive task assistance for inspectors in real time. In this paper, a combined AR+AI system is introduced to assist brackets inspection in a more intelligent yet efficient manner. Especially, AR-based Mask R-CNN is proposed by skillfully integrating markerless AR into deep learning-based instance segmentation to generate more accurate and fewer region proposals, and thus alleviates the computation load of the deep learning program. Based on this, brackets segmentation can be performed robustly and efficiently on mobile devices such as smartphones or tablets. By using the proposed system, CAD model checking can be automatically performed between the segmented physical brackets and the corresponding virtual brackets rendered by AR in real time. Furthermore, the inspection results can be directly projected on the corresponding physical brackets for the convenience of maintenance. To verify the feasibility of the proposed method, experiments are carried out on a full-scale mock-up of C919 aircraft main landing gear cabin. The experimental results indicate that the inspection accuracy is up to 97.1%. Finally, the system has been deployed in the real C919 aircraft final-assembly workshop. The preliminary evaluation reveals that the proposed real-time AR-assisted intelligent inspection approach is effective and promising for large-scale industrial applications.