Alloy Cast Product Defect Detection Based on Object Detection

2021 ◽  
Author(s):  
Chih-Hsueh Lin ◽  
Chia-Wei Ho ◽  
Guo-Hsin Hu ◽  
Po-Chun Kuo ◽  
Chia-Yen Hu
Keyword(s):  
Object Detection ◽  
Defect Detection ◽  
Cast Product ◽  
Product Defect ◽  
Alloy Cast

FPP detector for small product defect detection

2021 ◽  
Vol 37 (1) ◽  
pp. 10
Author(s):  
Zihao Huang ◽  
Hong Xiao ◽  
Tao Wang ◽  
Junhao Zhou
Keyword(s):  
Defect Detection ◽  
Product Defect ◽  
Small Product

scholarly journals MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network

2020 ◽  
Vol 12 (22) ◽  
pp. 9785
Author(s):  
Kisu Lee ◽  
Goopyo Hong ◽  
Lee Sael ◽  
Sanghyo Lee ◽  
Ha Young Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Structural Integrity ◽  
Residential Building ◽  
Training Environment ◽  
Building Facade ◽  
Building Facades

Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.

scholarly journals Deep Active Learning for Surface Defect Detection

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1650 ◽  
Author(s):  
Xiaoming Lv ◽  
Fajie Duan ◽  
Jia-Jia Jiang ◽  
Xiao Fu ◽  
Lin Gan
Keyword(s):  
Active Learning ◽  
Object Detection ◽  
Defect Detection ◽  
Surface Defect ◽  
Candidate List ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Framework ◽  
Surface Defect Detection ◽  
Sampling Scale

Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data.

scholarly journals Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

2021 ◽  
Vol 66 (3) ◽  
pp. 2493-2507
Author(s):  
Hyun Kyu Shin ◽  
Si Woon Lee ◽  
Goo Pyo Hong ◽  
Sael Lee ◽  
Sang Hyo Lee ◽  
...  
Keyword(s):  
Object Detection ◽  
Defect Detection ◽  
Detection Method ◽  
Parking Lots ◽  
Detection Model ◽  
Image Object Detection ◽  
Image Object

Cecid Fly Defect Detection in Mangoes Using Object Detection Frameworks

2021 ◽  
pp. 205-216
Author(s):  
Maria Jeseca C. Baculo ◽  
Conrado Ruiz ◽  
Oya Aran
Keyword(s):  
Object Detection ◽  
Defect Detection
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