The research addresses a practical bottleneck in display manufacturing: inspectors can label an entire image as defective or defect-free relatively quickly, but tracing every defect boundary with boxes or polygons is slow and costly. The proprietary dataset contained 18,804 images at 1,392 by 988 pixels, divided into 12,222 training images, 941 validation images and 5,641 test images. It included nine defect classes and 745 defect-free examples. Images originating from the same physical panel, manufacturing lot or contiguous production period were kept within a single split, reducing the risk that correlated views of a panel would leak information from training into testing.

Training images carried image-level class annotations rather than dense boundary coordinates. Detailed pixel-level polygon references were used for the separate validation and test subsets to tune activation thresholds and calculate spatial-overlap metrics. This tiered protocol allowed the core detector to learn from comparatively inexpensive labels while retaining ground-truth boundaries for evaluation. The authors estimated that their approach could reduce manual annotation time by approximately 71% relative to a baseline procedure in which operators traced multi-vertex polygons around every defect across the data repository.

At the center of the method is a division of labor among several processing routes. One branch screens full images, another estimates the likely center of a defect through class activation mapping, and a third examines local regions at their native pixel density. The activation-mapping models were PYLON-RN50E, built around a ResNet-50 encoder and pyramid attention, and DenseNet-201 with Guided Grad-CAM. Their heatmaps usually emphasize the most discriminative portion of an anomaly rather than its complete outline, so their role is to anchor a search rather than create a perfect defect mask. This distinction is important because overlap with pixel-level ground truth remained modest even when the resulting classification was strong: locating a useful defect core can support recognition without reproducing every boundary pixel.

The researchers also built a classical Canny-Harris region-of-interest detector as an offline reference. It compares a defect image with 138 defect-free backgrounds, extracts edges and corner responses, combines the differences and uses density-based spatial clustering to locate the densest candidate region. The approach captured structural cues that could complement deep models, but it required about 1.056 seconds to score the backgrounds and extract a region from each 1,392 by 988 image, with a larger image taking roughly three times as long. By contrast, the PYLON-RN50E activation-mapping branch processed an image in 17 milliseconds and allocated about 2,048 MiB of video memory at peak load. The authors therefore position Canny-Harris as a calibration or reference tool, while the deep branch is used for faster online inspection.

Threshold selection exposed a pronounced trade-off between sensitivity and false alarms. At their lowest tested activation threshold, DenseNet-201-GGC generated false detections on 58.40% of clean images and PYLON-RN50E on 84.15%, because weak background responses remained in the heatmaps. Raising the thresholds sharply improved performance on defect-free panels. At thresholds of 34 and 213, respectively, DenseNet-201-GGC achieved 98.85% specificity and a 1.15% false-positive rate, while PYLON-RN50E reached 99.15% specificity and a 0.85% false-positive rate. Pushing PYLON-RN50E to a threshold of 249 lowered the false-positive rate further to 0.40%, but its coverage of defect regions collapsed to 2.13%, showing why the threshold that produces the fewest clean-image alarms is not automatically the most useful. The selected settings balanced background suppression, defect coverage and the requirements of downstream classification.

To recover features that might disappear during full-image processing, the framework divides a panel into overlapping windows and classifies the resulting crops with EfficientNetV2-s, EfficientNetV2-s with convolutional block attention, or PYLON-RN50E. A 1,392 by 988 image produces 117 windows of 224 by 224 pixels when the stride is 100 pixels, although PYLON-RN50E uses 256 by 256 windows. The final sliding-window result is determined by the window with the highest defect confidence; if no window indicates a defect, the image is classified as defect-free. This mechanism proved important: a basic majority vote without the sliding-window models reached 91.19% accuracy, while including their three predictions raised the unoptimized vote to 95.81%. A search over 255 possible model subsets on the validation set then excluded the ROI-trained EfficientNetV2-s with attention, producing a locked optimized majority-voting ensemble that reached 96.33% on the independent test set.

The optimized system reported macro-averaged precision of 95.46%, recall of 95.98%, an F1 score of 95.72% and an area under the precision-recall curve of 0.96. The best individual classifier, EfficientNetV2-s trained on regions of interest, achieved 89.19% accuracy, making the ensemble’s advantage 7.14 percentage points. Across 10 training runs with different random seeds, the reported standard deviation was 0.35 for the ensemble. A Wilcoxon signed-rank comparison with the strongest individual baseline gave a p-value below 0.05, indicating a statistically significant difference under the study’s testing protocol. The authors attributed the improvement to two complementary effects: the sliding window recovered spatial information lost during downscaling, while architectural diversity allowed voting among DenseNet, EfficientNet and PYLON models to suppress isolated errors.

Running the branches concurrently required substantial hardware. The full parallel configuration used 21,154 MiB of GPU memory and completed inference in 425.3 milliseconds, compared with about 1.48 seconds for an equivalent sequential arrangement that used less memory. A more moderate configuration ran the sliding-window classifiers concurrently and the remaining branches sequentially, limiting peak allocation to 13,284 MiB. In a separate operational configuration that omitted the sliding-window classifiers, the reported per-image inference time was 41.4 milliseconds with 5,822 MiB of allocated memory. The experiments ran on a workstation with an AMD EPYC 9654P processor, 128 GiB of RAM and an Nvidia RTX 3090 GPU with 24,576 MiB of memory, so the results do not establish performance on lower-power embedded hardware.

Tests on two public datasets suggested that the architecture was not limited to the green-background display imagery used in the primary experiment. On the NEU hot-rolled steel dataset, the framework reported 95.88% accuracy and a 95.10% F1 score, compared with 86.50% and 85.12% for RT-DETR-L and 94.18% and 93.60% for Faster R-CNN. On the DAGM 2007 texture dataset, it reported 96.05% accuracy and a 95.42% F1 score; the corresponding Faster R-CNN results were 92.80% and 91.95%. The public benchmarks were deliberate stress tests outside the system’s manufacturing domain, and the qualitative examples still showed occasional false positives and class confusion. Static thresholds, changes in factory illumination, new material batches and diffuse sub-pixel scratches therefore remain practical limitations despite the strong aggregate scores.

The estimated 71% reduction in labeling hours depends on the assumed baseline workload and should not be read as a universal saving for every factory. The framework’s clearest demonstrated benefit is its combination of image-level training labels, a fast activation-map anchor, native-resolution window inspection and multi-model voting on the evaluated data. Its next step is to reduce memory use and latency, potentially through knowledge distillation and INT8 quantization, while testing on low-power edge devices and changing production environments. Even with those qualifications, the study presents a promising way to make automated optical inspection less dependent on exhaustive manual tracing while retaining the local detail needed to find small defects on display panels.

Subject of Research: Weakly supervised computer-vision framework for detecting defects in TFT-LCD manufacturing

Article Title: An annotation-efficient parallel processing framework for robust defect detection in TFT-LCD manufacturing

Article References: Ibrahim, M. I., Khanali, P., Sajid, S., Wanly, M., Weber, S., Drescher, B., Liu, C., & Mahmud, A. A. (2026). An annotation-efficient parallel processing framework for robust defect detection in TFT-LCD manufacturing. Results in Engineering, 32, Article 113476. https://doi.org/10.1016/j.rineng.2026.113476

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113476

Keywords: TFT-LCD manufacturing, industrial defect detection, weakly supervised learning, automated optical inspection, class activation mapping, sliding-window analysis, ensemble learning, computer vision, industrial quality control, annotation efficiency, annotation-efficient, parallel