scholarly journals Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Huaiguang Liu ◽  
Wancheng Ding ◽  
Qianwen Huang ◽  
Li Fang
Keyword(s):  
Neural Network ◽  
Solar Cell ◽  
Convolutional Neural Network ◽  
Silicon Wafer ◽  
Defect Detection ◽  
Detection Method ◽  
Detection Accuracy ◽  
Cell Component ◽  
Original Algorithm ◽  
Detection Speed

The defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the high pixel SCC image detection, each silicon wafer image was segmented based on local difference extremum of edge projection (LDEEP). Secondly, in order to detect the defects with small size or weak edges in the silicon wafer, an improved lightweight CNN model with deep backbone feature extraction network structure was proposed, as the enhancing feature fusion layer and the three-scale feature prediction layer; the model provided more feature detail. The final experimental results showed that the improved model achieves a good balance between the detection accuracy and detection speed, with the mean average precision (mAP) reaching 87.55%, which was 6.78% higher than the original algorithm. Moreover, the detection speed reached 40 frames per second (fps), which meets requirements of precision and real-time detection. The detection method can better complete the defect detection task of SCC, which lays the foundation for automatic detection of SCC defects.

scholarly journals Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network

2020 ◽  
Vol 10 (14) ◽  
pp. 4720 ◽  
Author(s):  
Zhiqiang Teng ◽  
Shuai Teng ◽  
Jiqiao Zhang ◽  
Gongfa Chen ◽  
Fangsen Cui
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Structural State ◽  
Detection Method ◽  
Steel Frame ◽  
Engineering Practice ◽  
Detection Accuracy ◽  
Vibration Signals

The traditional methods of structural health monitoring (SHM) have obvious disadvantages such as being time-consuming, laborious and non-synchronizing, and so on. This paper presents a novel and efficient approach to detect structural damages from real-time vibration signals via a convolutional neural network (CNN). As vibration signals (acceleration) reflect the structural response to the changes of the structural state, hence, a CNN, as a classifier, can map vibration signals to the structural state and detect structural damages. As it is difficult to obtain enough damage samples in practical engineering, finite element analysis (FEA) provides an alternative solution to this problem. In this paper, training samples for the CNN are obtained using FEA of a steel frame, and the effectiveness of the proposed detection method is evaluated by inputting the experimental data into the CNN. The results indicate that, the detection accuracy of the CNN trained using FEA data reaches 94% for damages introduced in the numerical model and 90% for damages in the real steel frame. It is demonstrated that the CNN has an ideal detection effect for both single damage and multiple damages. The combination of FEA and experimental data provides enough training and testing samples for the CNN, which improves the practicability of the CNN-based detection method in engineering practice.

scholarly journals High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1206 ◽  
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
High Speed ◽  
Earth Observation ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Speed

As an active microwave sensor, synthetic aperture radar (SAR) has the characteristic of all-day and all-weather earth observation, which has become one of the most important means for high-resolution earth observation and global resource management. Ship detection in SAR images is also playing an increasingly important role in ocean observation and disaster relief. Nowadays, both traditional feature extraction methods and deep learning (DL) methods almost focus on improving ship detection accuracy, and the detection speed is neglected. However, the speed of SAR ship detection is extraordinarily significant, especially in real-time maritime rescue and emergency military decision-making. In order to solve this problem, this paper proposes a novel approach for high-speed ship detection in SAR images based on a grid convolutional neural network (G-CNN). This method improves the detection speed by meshing the input image, inspired by the basic thought of you only look once (YOLO), and using depthwise separable convolution. G-CNN is a brand new network structure proposed by us and it is mainly composed of a backbone convolutional neural network (B-CNN) and a detection convolutional neural network (D-CNN). First, SAR images to be detected are divided into grid cells and each grid cell is responsible for detection of specific ships. Then, the whole image is input into B-CNN to extract features. Finally, ship detection is completed in D-CNN under three scales. We experimented on an open SAR Ship Detection Dataset (SSDD) used by many other scholars and then validated the migration ability of G-CNN on two SAR images from RadarSat-1 and Gaofen-3. The experimental results show that the detection speed of our proposed method is faster than the existing other methods, such as faster-regions convolutional neural network (Faster R-CNN), single shot multi-box detector (SSD), and YOLO, under the same hardware environment with NVIDIA GTX1080 graphics processing unit (GPU) and the detection accuracy is kept within an acceptable range. Our proposed G-CNN ship detection system has great application values in real-time maritime disaster rescue and emergency military strategy formulation.

scholarly journals Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 576
Author(s):  
Kaihua Zhang ◽  
Haikuo Shen
Keyword(s):  
Solder Joint ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Set ◽  
Electronic Products ◽  
Original Algorithm

The miniaturization and high integration of electronic products have higher and higher requirements for welding of internal components of electronic products. A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection of a connector has gradually attracted people’s attention with the development of image detection algorithm. The traditional solder joint detection method of connector adopts manual detection or automatic detection methods, which is inefficient and not safe enough. With the development of deep learning, the application of a deep convolutional neural network to target detection has become a research hotspot. In this paper, a data set of connector solder joint samples was made and the number of image samples was expanded to more than 3 times of the original by using data augmentation. Clustering generates anchor boxes and transfer learning with ResNet-101 were fused, so an improved faster region-based convolutional neural networks (Faster RCNN) algorithm was proposed. The experiment verified that the improved algorithm proposed in this paper had a great improvement in all aspects compared with the original algorithm. The average detection accuracy of this method can reach 94%, and the detection rate of some defects can even reach 100%, which can completely meet the industrial requirements.

scholarly journals Detection of Rice Diseases by the Fusion of Optimization based K-means Clustering Algorithm and Faster Region based Convolutional Neural Network

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 693-708
Author(s):  
A. Pushpa Athisaya Sakila Rani ◽  
N. Suresh Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Minimum Distance ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Median Filter ◽  
Plant Diseases ◽  
Detection Accuracy ◽  
Leaf Lesion ◽  
Rice Disease

One of the most important food crops in the world is rice, which is highly affected by various plant diseases and pests. Even though there are so many methods to address the concern, detection accuracy is a hectic challenge, which needs to be boosted for an enjoyable farming environment. In the present study a rice disease detection technique was implemented by the fusion of Sailfish optimization – K-means (SCM-KM) and the Faster Region Based Convolutional Neural Network (Faster R-CNN) method. For the optimization of the KM clustering method, Sailfish Optimizer was coupled with the Maximum and Minimum distance algorithm, as well as Chaos theory. The 2D Filtering Mask and Weighted Multilevel Median Filter(2DFM-AMMF) were used to eliminate the sounds. With the aid of the Faster 2D-Otsu technique, the target leaf lesion was segmented from the image. The SCM-KM method is used for detection of rice disease. The Rice diseases were characterized and classified by Region Proposal Networks (RPN) and Faster R-CNN method. Comparative analysis of the SCM-KM+ Faster R-CNN method was performed using the metrics sensitivity, accuracy, and specificity. The proposed detection method produced elevated performance over similar bench marking frameworks.

scholarly journals A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Danyang Zheng ◽  
Liming Li ◽  
Shubin Zheng ◽  
Xiaodong Chai ◽  
Shuguang Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
High Speed ◽  
Surface Defects ◽  
Detection Method ◽  
Experimental Tests ◽  
Deep Convolutional Neural Network ◽  
Railway Track ◽  
Model Based

As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.

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