  Detecting subtle defects in window frames, including dents and scratches, is
vital for upholding product integrity and sustaining a positive brand
perception. Conventional machine vision systems often struggle to identify
these defects in challenging environments like construction sites. In contrast,
modern vision systems leveraging machine and deep learning (DL) are emerging as
potent tools, particularly for cosmetic inspections. However, the promise of DL
is yet to be fully realized. A few manufacturers have established a clear
strategy for AI integration in quality inspection, hindered mainly by issues
like scarce clean datasets and environmental changes that compromise model
accuracy. Addressing these challenges, our study presents an innovative
approach that amplifies defect detection in DL models, even with constrained
data resources. The paper proposes a new defect detection pipeline called
InspectNet (IPT-enhanced UNET) that includes the best combination of image
enhancement and augmentation techniques for pre-processing the dataset and a
Unet model tuned for window frame defect detection and segmentation.
Experiments were carried out using a Spot Robot doing window frame inspections
. 16 variations of the dataset were constructed using different image
augmentation settings. Results of the experiments revealed that, on average,
across all proposed evaluation measures, Unet outperformed all other algorithms
when IPT-enhanced augmentations were applied. In particular, when using the
best dataset, the average Intersection over Union (IoU) values achieved were
IPT-enhanced Unet, reaching 0.91 of mIoU.
