# Detecting and Classifying Defective Products in Images Using YOLO

arXiv: https://arxiv.org/abs/2412.16935 · HTML: https://mlsanity.com/paper/2412.16935/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 13 of 13)

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- [Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model](https://mlsanity.com/paper/2410.17545/) (2024)
- [Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks](https://mlsanity.com/paper/2403.08499/) (2024)
- [Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning](https://mlsanity.com/paper/2409.02425/) (2024)

## Built on by (most cited first, 2 of 2)

- [Generating Multimodal Images with GAN: Integrating Text, Image, and Style](https://mlsanity.com/paper/2501.02167/) (2025)
- [Infectious Bovine Pinkeye Detection Using Computer Vision and Imbalance-Aware Learning](https://mlsanity.com/paper/2609.23714/) (2026)

## Abstract (arXiv)

With the continuous advancement of industrial automation, product quality inspection has become increasingly important in the manufacturing process. Traditional inspection methods, which often rely on manual checks or simple machine vision techniques, suffer from low efficiency and insufficient accuracy. In recent years, deep learning technology, especially the YOLO (You Only Look Once) algorithm, has emerged as a prominent solution in the field of product defect detection due to its efficient real-time detection capabilities and excellent classification performance. This study aims to use the YOLO algorithm to detect and classify defects in product images. By constructing and training a YOLO model, we conducted experiments on multiple industrial product datasets. The results demonstrate that this method can achieve real-time detection while maintaining high detection accuracy, significantly improving the efficiency and accuracy of product quality inspection. This paper further analyzes the advantages and limitations of the YOLO algorithm in practical applications and explores future research directions.