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Defect Detection Based on Vision Sensors

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Dear Colleagues,

In the era of Industry 4.0 and smart manufacturing, ensuring product quality and operational safety is paramount. Automated defect detection has emerged as a critical technology across numerous industries, including manufacturing, aerospace, robotics, agriculture, and infrastructure monitoring. Traditional manual inspection methods are often slow, subjective, prone to human error, and unsuitable for high-volume production lines. The integration of advanced computer vision and a diverse array of sensors offers a powerful, non-destructive, and efficient solution to these challenges.

This Special Issue aims to gather the latest research and innovative applications that leverage the synergy of computer vision and multi-modal sensor data for automatic defect detection. We seek to explore how the fusion of visual data (2D/3D) with information from other sensors (e.g., thermal, hyperspectral, ultrasonic, LiDAR, IoT) can overcome the limitations of single-modality approaches. The goal is to create more robust, accurate, and intelligent systems capable of identifying anomalies, cracks, surface defects, and structural flaws in complex and demanding environments.

Dr. Pu Huang
Dr. Liuyong Chang
Dr. Xiangdong Ma
Guest Editors

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26 pages, 19915 KB

Open AccessArticle

Scan Path Optimization and YOLO-Based Detection for Defect Inspection of Curved and Glossy Surfaces

by Min-Gyu Kim, Chibuzo Nwabufo Okwuosa and Jang-Wook Hur

Sensors 2026, 26(10), 3026; https://doi.org/10.3390/s26103026 - 11 May 2026

Viewed by 817

Abstract

Product defect inspection is critical in industrial applications; however, it remains increasingly challenging in mass production environments, particularly for glossy or curved surface products. Conventional inspection of such surfaces typically relies on manual visual examination using gauges and operator judgment, which is time consuming and prone to inconsistency. This study proposes a robust defect detection framework for curved and reflective surfaces using a KEYENCE displacement laser sensor. The system integrates the Dijkstra algorithm, the Nearest Neighbor Algorithm, and a Genetic Algorithm to optimize the laser scanning path for structured image data generation. To validate the proposed framework, datasets were generated from both healthy and defective samples and used to train multiple deep learning models. A comparative analysis was conducted using YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. Experimental results demonstrate that YOLOv11 achieved the best overall performance, attaining an mAP50 score of 0.844 while also exhibiting lower computational complexity and faster inference. Full article

(This article belongs to the Special Issue Defect Detection Based on Vision Sensors)

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20 pages, 5699 KB

Open AccessArticle

An Improved YOLOv8 Detection Algorithm Based on Screen Printing Defect Images

by Shuqin Wu, Xinru Dong, Qiang Da, Meiou Wang, Yuxuan Sun, Ge Ge, Jinge Ma, Jiajie Kang, Yu Yao and Shubo Shi

Sensors 2026, 26(5), 1604; https://doi.org/10.3390/s26051604 - 4 Mar 2026

Viewed by 605

Abstract

Micro-defects, such as ink spots, scratches, and sintering formed during the screen printing process of photovoltaic cells, significantly impair module performance. Traditional machine vision methods exhibit limited detection efficiency and high false-positive and missed-detection rates, while existing deep learning algorithms struggle to achieve accurate and adaptive detection of small-target defects and background similar defects in complex industrial environments. This study proposes an enhanced defect detection methodology based on an improved YOLOv8 algorithm. A multi-focus image acquisition platform using primary and auxiliary CCDs was independently developed, integrating a high-frame-rate industrial camera and a high-resolution electron microscope, with an LED ring light employed to suppress reflections, thereby establishing a high-quality dataset covering three defect categories. The algorithm was optimized through multiple dimensions: the RepNCSPELAN4 module was incorporated into the backbone network to improve multi-scale feature fusion, and a novel wavelet transform-based WaveConv module was designed to replace traditional downsampling, thereby better preserving defect edges and texture details. The neck network integrates a lightweight shuffle attention mechanism and a new detail enhancement module to strengthen critical features while controlling model complexity. Additionally, a dedicated auxiliary detection head was added for spotting tiny ink dots. Experimental results demonstrate a marked improvement in performance: on the custom dataset, the improved model achieves a stable mean average precision of approximately 92%. Specifically, ink spot detection reached a precision of 84.9% and recall of 77.7%, effectively reducing missed small-target defects; sintering defect detection attained 98.9% precision and 100% recall, addressing previous misclassifications due to background similarity; and scratch detection precision improved to 92.2%. Visual comparisons confirm that the enhanced model effectively overcomes the limitations of the original approach. By constructing a specialized dataset and implementing targeted, coordinated optimizations to the YOLOv8 architecture, this study significantly enhances the accuracy and robustness of screen-printing defect detection in photovoltaic cells, providing an effective solution for real-time online quality inspection in smart manufacturing lines. Full article

(This article belongs to the Special Issue Defect Detection Based on Vision Sensors)

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