For surface defects in electronic water pump shells, the manual detection efficiency is low, prone to misdetection and leak detection, and encounters problems, such as uncertainty. To improve the speed and accuracy of surface defect detection, a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods. In this method, the MobileNetV3 module replaces the backbone network of YOLOv5s, depth-separable convolution is introduced, the parameters and calculations are reduced, and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy. A dataset of electronic pump shell defects is established, and the performance of the improved method is evaluated by comparing it with that of the original method. The results show that the parameters and FLOPs are reduced by 49.83% and 61.59%, respectively, compared with the original YOLOv5s model, and the detection accuracy is improved by 1.74%, which is an indication of the superiority of the improved method. To further verify the universality of the improved method, it is compared with the results using the original method on the PASCALVOC2007 dataset, which verifies that it yields better performance. In summary, the improved lightweight method can be used for the real-time detection of electronic water pump shell defects. 

A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

Inspeksi kualitas produk berbasis citra merupakan hal yang penting bagi industri manufaktur. Tugas tersebut sebagian besar masih dilakukan oleh manusia yang memiliki unit per hour rendah. Metode konvensional untuk inspeksi citra masih mengandalkan metode berbasis fitur, yang memiliki masalah sulitnya generalisasi dan ekstraksi fitur. Masalah tersebut diatasi dengan metode CNN, tetapi CNN membutuhkan data yang besar dan waktu training yang lama. Penggunaan pre-trained model dan augmentasi citra dapat menyelesaikan permasalahan pada metode-metode sebelumnya. Namun, belum ada penelitian yang secara lengkap meneliti dan membandingkan performa berbagai pre-trained model dan variasi augmentasi citra untuk tugas inspeksi citra kualitas produk manufaktur.Proses penelitian menggunakan dataset sekrup berjenis multi class dan binary class pada 33 jenis pre-trained model dan 8 jenis augmentasi citra. Pengujian pre-trained model menggunakan dataset gabungan seluruh jenis augmentasi citra. Model dengan akurasi tertinggi adalah EfficientNetV2-L untuk dataset multi class (97.8%) dan VGG-19 untuk dataset binary class (96.5%). Augmentasi citra dengan signifikansi tertinggi terhadap performa model adalah blur, dengan akurasi 81.1% pada multi class dan 92% pada binary class. Keseluruhan proses pengujian pre-trained model dan augmentasi citra berjalan dengan baik. Kata kunci—Inspeksi kualitas produk, Pre-trained model, Augmentasi citra

Penggunaan Pre-trained Model untuk Klasifikasi Kualitas Sekrup

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

/pdf/plastic-gasket-defect-detection-based-on-transfer-learning-52i3cqvk9n.pdf

Plastic Gasket Defect Detection Based on Transfer Learning

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Plastic Gasket Defect Detection Based on Transfer Learning