TY - GEN
T1 - Defect Detection on Metal Laptop Cases by Up-sampling and Down-sampling Method
AU - Lin, Hsien I.
AU - Sanjaya, Satrio Dwi
AU - Rupa, Landge
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Defect detection is a crucial process in an industry that prioritizes quality. With the rapid rise in industrial automation, most companies are shifting their focus from manual to automatic detection methods. Manual inspections can be performed in a variety of ways, but these methods do have some drawbacks. They are time-consuming and require intensive labor. To solve this problem, we proposed deep learning algorithms to detect the defects automatically. Firstly, we used a 6 -degree freedom manipulator to collect defect data, which were then identified and detected using various approaches. In this study, we compare our proposed model with another deep learning models such as YOLOv3, YOLOv4, and SSD. The proposed model outperforms these conventional deep-learning models using downsampling with dilation convolution to produce the high-semantic feature map. Combining the prediction from both upsampling and downsampling operations boosts the accuracy of the model. The accuracy of the SSD, YOLOv3, YOLOv4, and our proposed method are 76 %, 63 %, 62 %, and 81 %, respectively. The accuracy of our proposed model is 81 % after evaluating these notable algorithms. The mean Average Precision (mAP) of the SSD, YOLOv3, YOLOv4, and our proposed method are 61 %, 63 %, 60 %, and 65 %, respectively. The mAP of our proposed model is 6 5 % after evaluating various types of defects.
AB - Defect detection is a crucial process in an industry that prioritizes quality. With the rapid rise in industrial automation, most companies are shifting their focus from manual to automatic detection methods. Manual inspections can be performed in a variety of ways, but these methods do have some drawbacks. They are time-consuming and require intensive labor. To solve this problem, we proposed deep learning algorithms to detect the defects automatically. Firstly, we used a 6 -degree freedom manipulator to collect defect data, which were then identified and detected using various approaches. In this study, we compare our proposed model with another deep learning models such as YOLOv3, YOLOv4, and SSD. The proposed model outperforms these conventional deep-learning models using downsampling with dilation convolution to produce the high-semantic feature map. Combining the prediction from both upsampling and downsampling operations boosts the accuracy of the model. The accuracy of the SSD, YOLOv3, YOLOv4, and our proposed method are 76 %, 63 %, 62 %, and 81 %, respectively. The accuracy of our proposed model is 81 % after evaluating these notable algorithms. The mean Average Precision (mAP) of the SSD, YOLOv3, YOLOv4, and our proposed method are 61 %, 63 %, 60 %, and 65 %, respectively. The mAP of our proposed model is 6 5 % after evaluating various types of defects.
KW - Convolutional Neural Network (CNN)
KW - Deep learning
KW - Metallic defect
UR - http://www.scopus.com/inward/record.url?scp=85206488520&partnerID=8YFLogxK
U2 - 10.1109/INISTA62901.2024.10683851
DO - 10.1109/INISTA62901.2024.10683851
M3 - Conference contribution
AN - SCOPUS:85206488520
T3 - 18th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2024
BT - 18th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2024
A2 - Badica, Costin
A2 - Ivanovic, Mirjana
A2 - Koprinkova-Hristova, Petia
A2 - Leon, Florin
A2 - Manolopoulos, Yannis
A2 - Yildirim, Tulay
A2 - Ucar, Ayseg�l
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2024
Y2 - 4 September 2024 through 6 September 2024
ER -