Soldered Dots Detection of Automobile Door Panels based on Faster R-CNN Model
- Resource Type
- Conference
- Authors
- You, Wenjing; Chen, Liding; Mo, Zhimin
- Source
- 2019 Chinese Control And Decision Conference (CCDC) Control And Decision Conference (CCDC), 2019 Chinese. :5314-5318 Jun, 2019
- Subject
- General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Feature extraction
Proposals
Training
Automobiles
Object detection
Inspection
Deep learning
Faster R-CNN
Deep Learning
Convolutional Neural Network
- Language
- ISSN
- 1948-9447
This paper mainly introduces a Faster R-CNN algorithm to identify the soldered dots of automobile door panels. It compares the effect of state-of-the-art algorithm on identification of soldered dots and proposes a better calculating method for the project. The dataset in this research was a collection of more than 800 unwelded inner door panels collected by cameras, including 500 photos of entire door panels and 300 partials. Detection of soldered dots is based on Faster R-CNN, extracting the feature maps from image via the VGG16 convolution neural network. Region proposal networks generate region proposals for feature maps. ROI pooling extracts proposal feature maps from the input feature maps and proposals. The fully connected layer utilizes the proposal feature maps to calculate the classes of proposals, while the bounding box regressor obtains the exact location of predicted boxes. The experimental results show that both Faster R-CNN and YOLOv3 can be applied to small soldered dots detection. YOLOv3 has a faster speed and it is suitable for real-time detection. Faster R-CNN has high detection accuracy and it is suitable for non-real-time high-precision detection. In addition, Faster R-CNN is more generalized which should be given priority in complex scenarios.