Research on Automatic Identification of Drainage Network Defects based on Transformer and CNN

As the scale of distribution networks expand, existing defect identification methods face numerous challenges, including limitations in single-modal feature identification, insufficient cross-modal information fusion, and the lack of a multi-stage feedback mechanism. To address these issues, we first propose a joint perception of image and voiceprint features based on bidirectional coupled attention, which enhances deep interaction across modalities and overcomes the shortcomings of traditional methods in cross-modal fusion. Secondly, a defect identification and diagnosis method of distribution network electrical equipment based on two-stage convolutional neural networks (CNN) is introduced, which makes the network pay more attention to typical and frequent defects, and enhances defect diagnosis accuracy and robustness. The proposed algorithm is compared with two baseline algorithms. Baseline 1 is a long short term memory (LSTM)-based algorithm that performs separate feature extraction and processing for image and voiceprint signals without coupling the features of the two modalities, and Baseline 2 is a traditional CNN algorithm that uses classical convolutional layers for feature learning and classification through pooling and fully connected layers. Compared with two baselines, simulation results demonstrate that the proposed method improves accuracy by 12.1% and 33.7%, recall by 12.5% and 33.1%, and diagnosis efficiency by 22.92% and 60.42%.

The core of comprehensive treatment effect of water environment in river basin lies in pipe network system. The blockage of urban drainage pipe network is one of the main reasons affecting the healthy operation of urban drainage network system. So the intelligent analysis method of urban drainage pipe network blockage diagnosis can quickly find the problems of urban drainage pipe network and improve the operation and maintenance efficiency of urban drainage pipe network. In this paper, through the analysis of rainfall data, rainfall and sewage pipe network flow variation regular pattern, the change range, the essay obtains the rain and sewage pipe network blockage diagnosis threshold. Combined with the urban intelligent drainage pipe network system, through the customized setting of pipe network blockage threshold, the rapid diagnosis and early warning of the blockage problem of the drainage pipe network are realized, and the healthy operation of the urban drainage network is guaranteed.

Identification method of typical defects in transmission lines based on YOLOv5 object detection algorithm

Defect identification based on parameter estimation of histogram in ultrasonic IR thermography

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask-RCNN (Mask Region-based Convolutional Neural Networks) and Center-Mask; 2. The independent semantic segmentation methods: U-net and Resnet-U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms.

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model RayNet for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

Detection of apple defect using laser-induced light backscattering imaging and convolutional neural network

Research on intelligent control theory and strategy of gas drainage pipe network based on graph theory

Affective social multimedia computing provides us the opportunity to improve our daily lives. Various things, such as devices in ubiquitous computing environments and autonomous vehicles in real environments considering human beings, can be controlled by analyzing and learning affective social big data. Deep learning is a core learning algorithm for autonomous control; however, it requires huge amounts of learning data, and the process of collecting various types of learning data is expensive. The collection limit of affective social videos for deep learning is resolved by analyzing affective social videos, such as YouTube and Closed Circuit Television (CCTV) videos collected in advance, and generating new affective social videos more as learning data without human beings autonomously controlling other cameras. The control signals of the cameras are generated by Convolutional Neural Network (CNN)-based end-to-end controls. However, images captured consecutively need to be analyzed to improve the quality of the generated control signals. This paper proposes a system that generates affective social videos for deep learning by Convolutional Recurrent Neural Network (CRNN)-based end-to-end controls. The extracted images in affective social videos are utilized for calculating the control signals based on the CRNN. Additional affective social videos are then generated by the extracted consecutive images and camera control signals. The effectiveness of the proposed method was verified in the experiments by comparing the results obtained using the proposed method with those obtained using the traditional CNN. The results showed that the accuracy of the control signals obtained using the proposed method was 56.30% higher than that of the control signals obtained using the traditional CNN.

Urban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the influence of non-defective areas and enhance the defect features. Then, Canny algorithm and the AND logical operation were used to extract the image of defect area. Next, the texture feature, edge feature, and HOG feature were combined to extract the feature of the defect area image. Finally, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was used to automatically identify and classify defect images. The experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method.

Maintaining both rider safety and vehicle dependability on motorbikes requires accurate problem detection. Using an improved ResNet architecture with Improved Sea Fish Optimization (ISFO) and Deep Convolutional Neural Networks (CNNs), this research proposes a sophisticated method for auditory defect identification in motorbikes. The machine ears start by gathering a wide range of audio frequency-based signal datasets from motorbike that span a range of failure scenarios and operational settings. To eliminate noise and identify distinguishing characteristics, these signals go through preprocessing. Then, to extract high-level features from the pre-processed signals, an improved ResNet architecture is used, supplemented with ISFO. By integrating both local and global information, the ResNet architecture's inclusion of ISFO makes it easier to iteratively update feature representations. To further improve the feature representations' discriminative power, Deep CNNs are used. The real-time defect detection system is designed specifically for motorbike uses the learned model. The trained model is used to interpret incoming acoustic data from motorcycle operations. This allows for the identification and categorization of various issues, such as engine misfires, irregularities in the valves, wear on the bearings, and clutch bearing failures. Experiments show that the proposed method is a good fit for precisely categorizing motorbike issues. Analyses conducted in comparison with baseline models demonstrate the superiority of the ResNet-ISFO and Deep CNN technique, demonstrating its resilience and efficiency across a range of fault situations and operational conditions. Overall, the proposed acoustic problem detection system is a potential approach for improving maintenance procedures while also assuring the safety and dependability of automobile engine. Its incorporation into standard maintenance operations can aid in proactive defect identification, reducing downtime and improving vehicle performance.

In pipeline ultrasonic testing, the accurate identification of defect types is a prerequisite for quantifying defect sizes. To this end, this paper proposes a defect identification algorithm based on the combination of Wavelet Packet Transform (WPT), Distance Evaluation Technique (DET) and GA-SVM to precisely identify defect boundaries. First, the ultrasonic echo signals are processed using WPT to more accurately extract defect information. Then, DET is employed to eliminate redundant feature parameters, which are sorted by sensitivity to determine the input feature parameters for the GA-SVM. On this basis, a self-developed ultrasonic testing experimental system is used to collect ultrasonic echo signals from Q235 steel pipes with cylindrical, spherical, and conical defects at different spatial orientations of the probe and the pipe. After processing and identifying the signals, it was found that the proposed algorithm achieves an average identification accuracy of 99% for different types of defects, thereby realising automatic identification of pipeline defects. The results provide new ideas and methods for the intelligent identification of volumetric defect types in pipeline ultrasonic internal testing and promote the development of industrial-grade ultrasonic internal detectors.

Weather is a phenomenon occurs in the earth’s atmosphere. Weather affects human daily activities, especially outdoor activities. Weather observations including rainfall observation in Indonesia conducted by Meteorology, Climatology, and Geophysical Agency (BMKG). BMKG facing a major problem in terms of rainfall data spatial density. The insufficient amount and unevenly distributed rainfall measurement instrument, are two main factors contributing to rainfall data special density problems. One of the very prominent methods to gain a larger amount of rainfall measurement location is using the image obtained from existing Closed Circuit Television (CCTV) spread over vast areas, especially in the Jakarta region. The approach to recognize and classify the rainfall in a certain area from the CCTV image used in this research is the Convolutional Neural Network (CNN) method. The image data was taken from CCTV located in Kamal, Kalideres, West Jakarta. The images taken is split into two categories, the one that shows a rainy day and the one that shows a clear day. These two categories of images will be used as sample data to train CNN, an effort to obtain a suitable model. By using the CNN method, it’s possible to recognize and classify the rainfall condition within an image based on the model. Python is an open-source programming language that widely used nowadays to run CNN. The image classification using this CNN, scored approximately 98.30% of accuracy, which means that the model is optimal to recognize and classify rainfall conditions in a certain area based on the CCTV images.

Automatic defect identification technology of digital image of pipeline weld