Defect Detection on Metal Laptop Cases by Up-sampling and Down-sampling Method

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.