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

Abstract

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.

Identification and Area Calculation of the Egg Mass of Spodoptera Frugiperda Based on Faster-RCNN

“…Common model evaluation indexes in target detection models include accuracy, precision, recall, etc. [18,24]. Accuracy is the proportion of the number of correct predictions to the number of observations.…”

“…The structure of Faster-RCNN shown in Fig. 4 includes deep full convolutional network, Region Proposal Networks (RPN), ROI Pooling module, target classification and positioning module [24][25][26] .…”

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

“…In our analysis, we found out that more than 42% of composed primary studies implementing NN are focused on CNN, for example, Lin et al (2019), Wang et al (2018) 2020), Cerezci et al (2020) or Jiang et al (2021). R-CNN algorithm is also often used (more than 13%), for example, in Tabernik et al (2019), Shi et al (2020a), Liyun et al (2020), Zhao et al (2020a), Zhang & Shen (2021) or Zhao et al (2021). Moreover, there are articles focusing on the implementation of Contextual Hopfield Neural Network (Chang et al, 2011), Sparse Convolutional Neural Networks (Bella et al, 2019), FCN (Zhang et al, 2019), ResNet50 (Konovalenko et al, 2020), VGG16 (Ihar et al, 2019), YOLOv3 (Yu et al, 2019).…”

Multi-Stage Feature Enhancement Pyramid Network for Detecting Objects in Optical Remote Sensing Images

“…In this case, earlier detection algorithms will be more complicated in design, and the detection effect cannot meet the actual demand. After more than a decade of development, deep neural networks have gradually matured, and many high-level network design solutions have emerged, becoming the mainstream algorithm for solving object detection problems [10][11][12][13][14][15][16][17][18][19][20]. Among these methods, Faster-RCNN [10] provides a new idea to accomplish the task of multi-category target detection for images on an efficient and high accuracy basis.…”