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Open research questions in Industrial Vision Systems and Defect Detection

65 unresolved questions extracted from the limitations and future-work sections of 277 Industrial Vision Systems and Defect Detection papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Variations in texture and reflectance of different chocolate brands. Limitations of traditional image analysis techniques in detecting scratches. Need for a more effective texture analysis technique.

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

  • The gap is the lack of automated systems for product detection in commercial sector companies. The gap is the lack of application of deep learning techniques for object detection in real-world commercial settings.

    Deep Learning-Based Application for Automatic Product Detection in a Commercial Sector Company in Trujillo · 2026 · DOI

  • However, in lithography review, merely determining whether an image contains a defect is insufficient for engineering inspection; models must also understand defect morphology, spatial location, and the potential causes supported by visible evidence.

    LDU-Bench: Multimodal LLM Evaluation for Lithography Defect Understanding under Layout-Varying Circuit Backgrounds · 2026

  • 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 manual inspection methods are time-consuming and prone to errors. There is a need for automated inspection systems that can provide fast and accurate analysis of components.

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

  • 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 study does not address the generalizability of the approach to other types of defects or manufacturing environments. The dataset is limited to 1,774 images and 10,475 annotated defect instances. The approach may not be suitable for real-time defect detection due to computational requirements.

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

  • Existing studies on PCB defect detection do not address the complexity of defects in realistic manufacturing conditions. There is a need for a robust and efficient defect detection approach for PCBAs.

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

  • 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

  • The challenge of predicting rare manufacturing failure with highly imbalanced datasets. The need for more effective defect detection systems in semiconductor manufacturing.

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

  • 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 traditional deep learning methods suffer from high false detection and miss rates. There is a need for a model that can improve detection accuracy and reduce false detection and miss rates.

    MAE-YOLO improves small object detection for intelligent inspection · 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 need for a reliable and efficient method for real-time edge quality assessment. The lack of a deep learning-based image classification method for edge quality assessment.

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

  • The scarcity and diversity of anomalous samples in industrial defect detection. The limitations of standard geometric or mixing-based augmentations in introducing realistic, label-preserving appearance variations.

    Few-Shot Industrial Defect Detection via Diffusion-Based Data Augmentation with Geometric Pattern Masks · 2026 · DOI

  • The lack of a system for real-time surface imaging and data set creation to detect surface defects in aluminum conductors. The need for a modular system that can be integrated into production lines.

    Design and Production of a Prototype Conveyor Belt for Real-Time Surface Imaging and Data Set Creation · 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 need for an integrated hardware–software complex for real-time detection and classification of wind turbine blade defects. The lack of a system that maintains sufficient diagnostic accuracy and real-time performance on resource-constrained hardware.

    Integrated hardware–software complex for wind turbine blade defect classification · 2026 · DOI

  • close and Future research will focus on extending the defect increasing dataset diversity under real taxonomy, operating conditions, integrating multispectral or thermal sensing, improving spatial localization accuracy, and further enhancing the neuro-fuzzy decision module for more reliable handling of ambiguous and previously unseen defect patterns.

    Integrated hardware–software complex for wind turbine blade defect classification · 2026 · DOI

  • The study only evaluates the performance of YOLOv10 and YOLOv11 architectures. 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

  • 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

  • The need for accurate and efficient car parts segmentation in the automotive industry. The limitations of prior segmentation models in terms of accuracy and efficiency.

    Optimizing YOLOv11 for Accurate Car Parts Segmentation in Automotive Industry Applications · 2026 · DOI

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65 open questions have been extracted from the limitations and future-work passages of 277 Industrial Vision Systems and Defect Detection papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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