Arc sag is an important parameter in the design and operation and maintenance of transmission lines and is directly related to the safety and reliability of grid operation. The current arc sag measurement method is inefficient and costly, which makes it difficult to meet the engineering demand for fast inspection of transmission lines. In view of this, this paper proposes an automatic spacer bar segmentation algorithm, CM-Mask-RCNN, that combines the CAB attention mechanism and MHSA self-attention mechanism, which automatically extracts the spacer bars and calculates the center coordinates, and combines classical algorithms such as beam method leveling, spatial front rendezvous, and spatial curve fitting, based on UAV inspection video data, to realize arc sag measurement with a low cost and high efficiency. It is experimentally verified that the CM-Mask-RCNN algorithm proposed in this paper achieves an AP index of 73.40% on the self-built dataset, which is better than the Yolact++, U-net, and Mask-RCNN algorithms. In addition, it is also verified that the adopted approach of fusing CAB and MHSA attention mechanisms can effectively improve the segmentation performance of the model, and this combination improves the model performance more significantly compared with other attention mechanisms, with an AP improvement of 2.24%. The algorithm in this paper was used to perform arc sag measurement experiments on 10 different transmission lines, and the measurement errors are all within ±2.5%, with an average error of −0.11, which verifies the effectiveness of the arc sag measurement method proposed in this paper for transmission lines.

当前工业化养殖胡瓜钝绥螨的产量巨大,导致现有人工品质管控方式无法及时准确地对螨虫养殖品质进行监测.设计研发一款螨虫监控系统,包含螨虫麦麸分离装置、显微视觉装置、改进的YOLOv5 螨虫检测模型.通过对YOLOv5 模型的网络结构进行分析,在原YOLOv5 模型上将嵌入ECANet注意力机制的MobileNetv3-Large网络作为主干特征提取网络,加强网络提取特征能力,进一步通过裁剪模型预测头的冗余部分,提升模型对中小目标检测性能.实验结果表明:改进后的M3ECA-YOLOv5-2H模型对比原YOLOv5 模型,在平均检测精度mAP50 和mAP75上提高了 0.68 和 4.62 个百分点;在模型大小上降低 18.9 MB,占用更低内存;在单张图片推理速度上提升 4 ms.此外对比其他 5 种通用目标检测模型,M3ECA-YOLOv5-2H模型在检测精度和检测速度等指标上整体表现更佳,具有很好的应用价值.

针对目前我国许多建筑施工过程中的骨料罐装仍然是由传统的简单电气控制配合人工进行工作,存在物料流量难把控、效率低、安全性差等问题,在对传统骨料散装机结构采用旋转阀机构改进的基础上,提出基于三菱PLC的骨料散装机精准下料控制系统设计方案,完成了结构设计和PLC控制系统的开发.该控制系统实现了骨料散装机原料下落连续化、自动化,并在市电断电时能够自动关阀.经实际生产验证,该骨料散装机运行良好,改善了工作效率并提高了建筑工地灌装施工的质量效益.

牙模 3D打印过程中出现的质量缺陷影响着牙模的外观和使用性能,严重时导致废品率高,造成时间、材料的浪费.为了减少牙模 3D打印的废品率,提出了一种基于YOLOv5 的牙模 3D打印实时缺陷检测方案:首先获取多维度牙模缺陷图片,采用切图分割与数据增强处理等方式制作数据集,然后通过构建YOLOv5 深度学习网络模型对牙模 3D打印图像进行迭代训练,最后通过YOLOv5 程序调用摄像头并使用训练后最佳权重值进行实时在线缺陷检测.通过实验对比,YOLOv5 深度学习网络模型的检测准确率要高于Faster R-CNN、YOLOv3、SSD这 3 种模型,其检测平均准确度高达 94.78%,平均检测时间为 21 ms.结果表明该方法能够检测牙模 3D打印过程中的质量缺陷问题.

针对胡瓜钝绥螨体积小、与粉螨相似、难以分类计数问题,提出一种基于YOLOv4胡瓜钝绥螨自动检测计数方案.在多时间段、分批次拍摄螨虫照片制作数据集,通过构建YOLOv4深度学习模型对螨虫图像进行特征提取和高精度分类检测.结果表明,基于YOLOv4的螨虫识别模型能在图像存在杂质且螨虫相连情况下准确检测胡瓜钝绥螨,平均检测单幅图片耗时约0.137 s.在测试集中对胡瓜钝绥螨检测精度达到99.45％,粉螨检测精度达到93.94％.对胡瓜钝绥螨和粉螨检测精度均值(mAP)达到96.69％,分别比YOLOv4-Tiny、YOLOv3、Faster-RCNN、EfficientDet模型高14.85、2.12、35.77、27.18百分点.设计螨虫GUI(图形用户界面)品质监控界面,方便检测人员操作.本研究提出的检测方案具有很高的应用价值,能够精准、快速识别胡瓜钝绥螨,可为胡瓜钝绥螨养殖质量检测提供技术支撑.

With the continuous change of artificial intelligence technology, the study of path planning for mobile robots is no longer limited to traditional path algorithms. Reinforcement learning, as an artificial intelligence algorithm with excellent performance in the field of path planning, has also gradually become the object of research on path planning. In order to find a fast path from the starting point to the end point of a mobile robot in a complex environment, reinforcement learning is used to find a valid path by sensing the environment and continuously receiving rewarding feedback through a method of trial and error learning like humans. Therefore, this paper selects two algorithms to verify their effectiveness on the basis of building a two-dimensional grid map. The final experimental results show that both reinforcement learning algorithms can eventually avoid obstacles and plan a valid path in a complex environment through continuous learning iterations. Q-Learning also reduces the path distance by 10% and the number of convergence iterations by 82% compared to Sarsa.

The problem of birds nesting in high-voltage towers has laid a major hidden danger to the safe operation of long-distance transmission lines. In the power line inspection, the image background of the bird’s nest is complex. Some nests are small and locally occluded, and it is difficult for existing object detection algorithms to detect with low computational effort under high accuracy. To solve the above problems, we recommend using KBN-YOLOv5. Based on YOLOv5, we use K-means algorithm to cluster the size of bird nest image to set the size of anchor frame. In the Backbone network, we replace the Focus and SPP modules with Conv and SPPF modules, respectively, and adjust the number of BottleneckCSP modules as well as their positions. Finally, in the Neck network, we improve the BottleneckCSP module combined with the Efficient Channel Attention (ECA) module for a more effective weight information distribution to accomplish a more detailed detection capability. The experimental results show that KBN-YOLOv5 demonstrates better performance with other mainstream algorithms in terms of combined performance of detection accuracy and model computation. Compared to baseline model (YOLOv5), the Recall and mAP values of KBN-YOLOv5 reach 92.3% and 96.0%, which improve 6.7% and 5.4%, respectively. In addition, KBN-YOLOv5 has good robustness, and the model computation is reduced by 6.7% while detection accuracy of the model is improved.

Due to the quality defects that occur in the current dental mold 3D printing process affecting the quality of the finished product and the existing algorithms' inability to detect defects in real time and low accuracy, a real-time defect detection scheme for dental mold 3D printing based on YOLOv4 deep learning model is proposed. The 3D printer and the design vision system built to print the dental mold acquire the image dataset of drawing and longitudinal defects, and turn the image dataset into a series of processing into the dataset required for YOLOv4 deep learning for training, prediction and real-time detection. After experimental comparison, YOLOv4 has the highest average detection accuracy of 93.74% and the fastest average detection time of 22 ms among the Faster-RCNN, EfficientDet, YOLOv3 and YOLOv4 deep learning network models. The results show that the method is capable of detecting defects in the 3D printing process in real time, and is advanced and practical for 3D printing defect detection.

Since the traditional PID control algorithm has many problems in parameter selection, it cannot meet specific requirements in engineering practice. However, the PID control algorithm after BP neural network tuning can realize adaptive learning and further improve the control ability, but due to the randomness of its initial weight selection, It is easy to lead to inconsistent training results and affect system stability. To solve these problems, this paper proposes to use the improved mayfly algorithm (IMA) to optimize BP neural network. By taking advantage of the powerful advantages of the improved mayfly algorithm in global search, the optimal position is found as the initial weight of BP neural network. Compared with the traditional method, the overshoot of IMA-BP-PID is only 0.28% in the second order control system, and the overshoot is greatly reduced without steady-state error, which can be better applied to the actual control system.

Automatic power line extraction from aerial images of unmanned aerial vehicles is one of the key technologies of power line inspection. However, the faint power line targets and complex image backgrounds make the extraction of power lines a greater challenge. In this paper, a new power line extraction method is proposed, which has two innovative points. Innovation point one, based on the introduction of the Mask RCNN network algorithm, proposes a block extraction strategy to realize the preliminary extraction of power lines with the idea of “part first and then the whole”. This strategy globally reduces the anchor frame size, increases the proportion of power lines in the feature map, and reduces the accuracy degradation caused by the original negative anchor frames being misclassified as positive anchor frames. Innovation point two, the proposed connected domain group fitting algorithm solves the problem of broken and mis-extracted power lines even after the initial extraction and solves the problem of incomplete extraction of power lines by background texture interference. Through experiments on 60 images covering different complex image backgrounds, the performance of the proposed method far exceeds that of commonly used methods such as LSD, Yolact++, and Mask RCNN. DSCPL, TPR, precision, and accuracy are as high as 73.95, 81.75, 69.28, and 99.15, respectively, while FDR is only 30.72. The experimental results show that the proposed algorithm has good performance and can accomplish the task of power line extraction under complex image backgrounds. The algorithm in this paper solves the main problems of power line extraction and proves the feasibility of the algorithm in other scenarios. In the future, the dataset will be expanded to improve the performance of the algorithm in different scenarios.