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Open research questions in Industrial Vision Systems and Defect Detection

109 gap statements mined from Industrial Vision Systems and Defect Detection papers in our 4.5M-paper local library, which holds 1,530 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • Traditional
Convolutional Neural Networks (CNNs) are limited by local receptive fields, making it dif
f
icult to capture long-range dependencies; meanwhile, while Vision Transformers excel at
global modeling, their representation of low-level pixel details is insufficient, easily leading
to missed detections or blurred segmentation of fine features such as small-scale defects
and defect edges.

    HDSNet:Hybrid CNN-Transformer Network for Defect Segmentation · 2026 · DOI

  • The data-hungry nature and black-box characteristics of deep learning models limit their performance and trustworthiness. The lack of interpretability in deep learning models makes it difficult to understand their decision-making processes.

    Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection · 2026

  • Future improvements should focus on advanced resampling methods, imbalance-specific algorithms, and ensemble learning techniques. The development of more robust and reliable machine learning models for defect prediction.

    Investigating the prediction of semiconductor wafer production through classification AI models · 2026 · DOI

  • Established Automated Optical Inspection systems lack semantic interpretability. The lack of semantic interpretability necessitates costly manual intervention for labeling and retraining.

    Autonomous Defect Classification in Manufacturing: A Novel Few-Shot Vision-Language Modeling Approach · 2026 · DOI

  • Data augmentation techniques can be used to increase the quantity of existing samples. Future research can proceed along the lines of addressing the few-shot learning problem.

    A Review of Surface Defect Detection Technologies Based on Machine Vision · 2026 · DOI

  • The few-shot learning problem is a significant challenge in practical applications. Deep learning models require a large volume of samples to complete training.

    A Review of Surface Defect Detection Technologies Based on Machine Vision · 2026 · DOI

  • The lack of automated, high-precision, and efficient round hole inspection technology. The limitations of traditional manual inspection methods, including low precision and poor data traceability. The need for a precision detection method that can achieve ultra-precise localization of edge points.

    Machine Vision-Based Precision Detection of Circular Holes Using Canny Threshold Optimization and Zernike Moments · 2026 · DOI

  • Future research may focus on optimizing the CNN architecture for improved classification accuracy and extending the proposed approach to classify other types of edge conditions and materials.

    Machine Vision Lab Experiment Based Convolutional Neural Network for Potential Intelligent Manufacturing Applications · 2026 · DOI

  • Future research may focus on expanding the capabilities of the developed system to gather more comprehensive data on various material types and surface structures. In particular, the thermal stability of the processor and graphics processor under long-term operating conditions has not yet been extensively tested.

    Design and Production of a Prototype Conveyor Belt for Real-Time Surface Imaging and Data Set Creation · 2026 · DOI

  • The analysis is limited to the PKU-Market-PCB dataset. The study does not consider other optimization algorithms or larger-scale models.

    Baskı Devre Kartı Kusur Tespiti için YOLOv10 ve YOLOv11 Mimarilerinin Karşılaştırmalı Analizi · 2026 · DOI

  • Future research will focus on expanding conveyor-belt and multi-source graphite ore datasets and deploying the model on real edge devices to further improve the generalization capability and practical application value of OGI-RT-DETR in complex industrial scenarios.

    OGI-RT-DETR: A Lightweight Vision-Sensing Model for Real-Time Graphite Ore Grade Recognition in Intelligent Sorting · 2026 · DOI

  • Future work will focus on extending the proposed ODCAE model toward online and domain-adaptive learning to improve robustness under domain shift. In particular, the performance degradation observed during cross-dataset evaluation highlights the challenges introduced by variations in machine configuration, geometry, and thermal behaviour.

    Print defect detection during laser powder bed fusion by one-dimensional convolutional autoencoder · 2026 · DOI

  • The system can be further enhanced by integrating advanced deep learning algorithms for improved defect classification and pattern recognition. Incorporating IoT technology can enable real-time monitoring and remote control of the inspection process.

    Development of a Machine Vision Based Non-Contact Metrology System for Inspection of Mechanical Components · 2026 · DOI

  • Traditional down quality inspection methods are plagued by low efficiency, strong subjectivity, and high error rates. The latest models do not necessarily achieve the optimal performance, with YOLOv26n and YOLOv26m exhibiting relatively low accuracy in the down detection task.

    A Method for Down Quality Inspection: YOLO-Based Impurity Detection and Quality Quantification · 2026 · DOI

  • Manual PCB inspection is inefficient, time-consuming, and prone to human error. Existing automated systems are costly and complex.

    PCB Defect Detection Using Image Processing for Component Analysis · 2026 · DOI

  • High-precision models have many parameters and complex structures, making them difficult to deploy. Existing methods do not achieve a balance between accuracy and efficiency.

    Lightweight metal surface defect detection algorithm based on pruning and knowledge distillation · 2026 · DOI

  • The study suggests evaluating larger-scale models, such as Large and Extra-Large. The analysis recommends testing different optimization algorithms, including Adam, AdamW, and Lion. The study proposes a systematic analysis of data augmentation techniques and real-time production line integration.

    Baskı Devre Kartı Kusur Tespiti için YOLOv10 ve YOLOv11 Mimarilerinin Karşılaştırmalı Analizi · 2026 · DOI

  • Conflicts of interest In this study, an artificial intelligence-based quality control system has been developed that enables the realtime and high-accuracy detection of defects in bolts moving on a conveyor belt on the production line.

    Real-Time Bolt Defect Detection with Artificial Intelligence in Industrial Quality Control Applications: Comparative Evaluation of YOLO Architectures · 2026 · DOI

  • For future work, we plan to expand the system capability by constructing larger and more diverse PCBA datasets covering various component types, assembly densities and complex or rare defect patterns.

    Deep learning-based multi-modal fusion with three-mode optical illumination for robust printed circuit board assembly defect detection · 2026 · DOI

  • Investigating the use of other deep learning models for down quality inspection, - Exploring the application of the proposed method in other industries, - Improving the accuracy and efficiency of the proposed…

    A Method for Down Quality Inspection: YOLO-Based Impurity Detection and Quality Quantification · 2026 · DOI

  • Inter-rater variability among experts, - Limited data for fine-tuning the model, - The need for domain experts' evaluative judgments, - The complexity of corrosion risk in bronze artifacts, - The limitation that a single indicator cannot capture the complexity of corrosion…

    An image segmentation-based quantitative method for assessing corrosion risk levels in bronze artifacts · 2026 · DOI

  • Further development of the proposed framework, - Application of the method to other types of cultural relics, - Investigation of other indicators for corrosion risk assessment, - Improvement of the model's generalization…

    An image segmentation-based quantitative method for assessing corrosion risk levels in bronze artifacts · 2026 · DOI

  • Physically correct models are rather complex and information on specific material parameters is needed - The proposed method is based on a simplified model of the defect…

    Synthetic defect geometries of cast metal objects modeled via 2d Voronoi tessellations · 2026 · DOI

  • To improve the accuracy of the proposed method - To apply the method to other types of defects and materials - To integrate the method with automated inspection…

    Synthetic defect geometries of cast metal objects modeled via 2d Voronoi tessellations · 2026 · DOI

  • Applying the proposed framework to other domains with similar texture analysis challenges. Improving the framework to handle significant variations in texture and reflectance.

    Region-Specific Adaptive GLCM Framework for Surface Scratch Detection in Heterogeneous Chocolate Products · 2026 · DOI

Most-cited papers in Industrial Vision Systems and Defect Detection

Most recent work

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Open questions raised by papers listed on this page include: (1) The lack of semantic interpretability necessitates costly manual intervention for labeling and retraining (“Autonomous Defect Classification in Manufacturing: A Novel Few-Shot…”, 2026); (2) Future research can proceed along the lines of addressing the few-shot learning problem (“A Review of Surface Defect Detection Technologies Based on Machine Vision”, 2026); (3) The lack of automated, high-precision, and efficient round hole inspection technology (“Machine Vision-Based Precision Detection of Circular Holes Using Canny…”, 2026); (4) Future research may focus on optimizing the CNN architecture for improved classification accuracy and extending the proposed approach to classify other types of edge conditions and materials (“Machine Vision Lab Experiment Based Convolutional Neural Network for Potential…”, 2026); (5) Future research may focus on expanding the capabilities of the developed system to gather more comprehensive data on various material types and surface structures (“Design and Production of a Prototype Conveyor Belt for Real-Time Surface…”, 2026). 109 gap statements have been mined from Industrial Vision Systems and Defect Detection papers in our 4.5M-paper local library, which holds 1,530 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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