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Industry requires high standards of quality and precision throughout all phases of the manufacturing process, from component procurement to the assembly of final products. Visual inspection is a critical step in the quality process. It is important to note that cases such as contamination or poor-quality images in customer reports may lead to customer complaints. Therefore, the need for automatic component classification through image analysis arises as a solution. Deep learning models, and specifically convolutional neural networks, have proven to be effective tools in offering accurate and rapid image classification. This project aims to develop a simple yet efficient tool that can be easily adapted to various visual inspection tasks. Pre-trained MobileNet and ResNet models were chosen and adapted to recognize both in-focus and out-of-focus images, to distinguish between clean and dirty parts from vacuum pumps, and to test the project's flexibility in detecting good and defective cables. The goal is to make the system accurate, user-friendly, and easily adaptable. One of the advantages of this work is that anyone can update the model using just a web browser, upload or take a photo of the image to be tested and receive the prediction. This proposal aims to remove technological barriers and apply artificial intelligence to a more common area for professionals and workers in the industrial area.

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Adaptation of Pre-Trained Neural Network Models for Inspection via Image Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Mártir Morales Juan Antonio,
  • Medina Muñoz Luis Arturo,
  • González López Samuel,
  • Valenzuela Soqui Luis Carlos,
  • Mayorquin Robles Jesús

摘要

Industry requires high standards of quality and precision throughout all phases of the manufacturing process, from component procurement to the assembly of final products. Visual inspection is a critical step in the quality process. It is important to note that cases such as contamination or poor-quality images in customer reports may lead to customer complaints. Therefore, the need for automatic component classification through image analysis arises as a solution. Deep learning models, and specifically convolutional neural networks, have proven to be effective tools in offering accurate and rapid image classification. This project aims to develop a simple yet efficient tool that can be easily adapted to various visual inspection tasks. Pre-trained MobileNet and ResNet models were chosen and adapted to recognize both in-focus and out-of-focus images, to distinguish between clean and dirty parts from vacuum pumps, and to test the project's flexibility in detecting good and defective cables. The goal is to make the system accurate, user-friendly, and easily adaptable. One of the advantages of this work is that anyone can update the model using just a web browser, upload or take a photo of the image to be tested and receive the prediction. This proposal aims to remove technological barriers and apply artificial intelligence to a more common area for professionals and workers in the industrial area.