Surface defect detection of steel strips based on classification priority YOLOv3-dense network

Zhu

et al. 2020

Coatings

Self Cite

Polytetrafluoroethylene (PTFE) is polymerized by tetrafluoroethylene, which has high corrosion resistance, self-lubrication and high temperature resistance. However, due to the large expansion coefficient, high temperature will gradually weaken the intermolecular bonding force of PTFE, which will lead to the enhancement of permeation absorption and the limitation of the application range of fluoroplastics. In order to improve the performance of PTFE, the modified polytetrafluoroethylene, filled by carbon materials and aramid fiber with different scales, is prepared through the compression and sintering. Moreover, the mechanical properties and wear resistance of the prepared composite materials are tested. In addition, the influence of different types of filler materials and contents on the properties of PTFE is studied. According to the experiment results, the addition of carbon fibers with different scales reduces the tensile and impact properties of the composite materials, but the elastic modulus and wear resistance are significantly improved. Among them, the wear rate of 7 μm carbon fiber modified PTFE has decreased by 70%, and the elastic modulus has increased by 70%. The addition of aramid fiber filler significantly reduces the tensile and impact properties of the composite, but its elastic modulus and wear resistance are significantly improved. Among them, the wear rate of the modified composite material with 3% alumina particles and 5% aramid pulp decreased by 68%, and the elastic modulus increased by 206%.

“…The surface of the product often produces some defects during processing [35]. In order to ensure the cleanliness of the PPTA surface, it is necessary to clean the PPTA first.…”

Section: Surface Treatment Of the Aramid Fibermentioning

confidence: 99%

Microstructure and Properties of Polytetrafluoroethylene Composites Modified by Carbon Materials and Aramid Fibers

Zhu

et al. 2020

Coatings

Self Cite

“…Precision, recall, and F 1 score were adopted to evaluate the performance of the proposed method. The recognition results can be divided into true positive (TP), false positive (FP), true negative (TN), and false negative (FN), respectively [39,40]. Precision is the percentage of the actual positive predictions among all predicted positive samples.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Thermal Defect Detection for Substation Equipment Based on Infrared Image Using Convolutional Neural Network

Wang

et al. 2021

Electronics

Self Cite

Thermal defects of substation equipment have a great impact on the stability of power systems. Temperature is crucial for thermal defect detection in infrared images. The traditional detection methods, which have low efficiency and poor accuracy, record the temperature of infrared images manually. In this study, a thermal defect detection method based on infrared images using a convolutional neural network (CNN) is proposed. Firstly, the improved pre-processing method is applied to reduce background information, and the region of interest is located according to the contour and position information, hence improving the quality of images. Then, the temperature values are segmented to establish the dataset (T-IR11), which contains 11 labels. Finally, the CNN model is constructed to extract features, and the support vector machine is trained for classification. To verify the effectiveness of the proposed method, precision, recall, and F1 score are adopted and 10-fold cross-validation is employed on the T-IR11 dataset. The results demonstrate that the accuracy of the proposed method is 99.50%, and the performance is superior to that of previous methods in terms of infrared images. The proposed method can realize automatic temperature recognition and equipment with thermal defects can be recorded systematically, which has significant practical value for defect detection in substation equipment.

“…e system can analyze and process the collected image data in parallel. Although the system guarantees real-time data processing functions, its versatility is low; literature [10] uses a new LED light source and FPGA embedded processing system to complete the design of a linear CCD steel plate defect detection system, which can store data by itself and function and display the test results on the screen. If there is a situation that does not meet the standard, the alarm function is completed, but as the function increases, it is not conducive to the production and development of the steel plate at the expense of reducing the production speed.…”

Section: Related Workmentioning

confidence: 99%

Steel Plate Defect Recognition of Deep Neural Network Recognition Based on Space-Time Constraints

Liu

Wang

2022

Advances in Multimedia

In order to improve the effect of real-time defect recognition in steel plate online production, this paper studies the method of steel plate defect recognition based on the deep neural network algorithm based on space-time constraints. Moreover, this paper improves the space-time constraint algorithm, optimizes the encryption structure of the traditional ABE scheme, and obtains a neural network feature recognition method based on space-time constraints. In order to process the massive image data stream generated instantaneously and ensure the real-time performance, accuracy, and stability of the detection system, this paper constructs a distributed parallel computing system structure based on the client/server (CC/S) model to obtain an intelligent recognition system. Through experimental research, it can be seen that the deep neural network recognition system based on space-time constraints proposed in this paper has a good effect in the recognition of steel plate defects.