7 research outputs found
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
Shadow removal is an essential task for scene understanding. Many studies
consider only matching the image contents, which often causes two types of
ghosts: color in-consistencies in shadow regions or artifacts on shadow
boundaries. In this paper, we tackle these issues in two ways. First, to
carefully learn the border artifacts-free image, we propose a novel network
structure named the dual hierarchically aggregation network~(DHAN). It contains
a series of growth dilated convolutions as the backbone without any
down-samplings, and we hierarchically aggregate multi-context features for
attention and prediction, respectively. Second, we argue that training on a
limited dataset restricts the textural understanding of the network, which
leads to the shadow region color in-consistencies. Currently, the largest
dataset contains 2k+ shadow/shadow-free image pairs. However, it has only 0.1k+
unique scenes since many samples share exactly the same background with
different shadow positions. Thus, we design a shadow matting generative
adversarial network~(SMGAN) to synthesize realistic shadow mattings from a
given shadow mask and shadow-free image. With the help of novel masks or
scenes, we enhance the current datasets using synthesized shadow images.
Experiments show that our DHAN can erase the shadows and produce high-quality
ghost-free images. After training on the synthesized and real datasets, our
network outperforms other state-of-the-art methods by a large margin. The code
is available: http://github.com/vinthony/ghost-free-shadow-removal/Comment: Accepted by AAAI 202
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies
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