Deep Learning Solutions

We enhance images, segment semantics, and classify defects using advanced deep learning techniques for accuracy.

Image Enhancement

Our image enhancement techniques improve quality, enabling better analysis and defect detection in real-world conditions.

A grainy and distorted image featuring a faint outline of a window casting dim light on a textured surface. The scene is obscured by horizontal noise lines, creating an abstract appearance.
A grainy and distorted image featuring a faint outline of a window casting dim light on a textured surface. The scene is obscured by horizontal noise lines, creating an abstract appearance.
Semantic Segmentation

We utilize semantic segmentation to accurately identify and classify defects, enhancing overall detection capabilities.

Our lightweight U-Net and ResNet hybrid model ensures efficient training and high detection accuracy in benchmarks.

Defect Classification
A close-up view of a leaf with significant decay, exhibiting multiple holes and a brown color. The leaf's edges are curled, and it is illuminated by bright backlighting, highlighting its textures against a blurred background.
A close-up view of a leaf with significant decay, exhibiting multiple holes and a brown color. The leaf's edges are curled, and it is illuminated by bright backlighting, highlighting its textures against a blurred background.
A close-up view of a green leaf with visible holes and imperfections, indicating potential insect damage. The background is blurred, creating a bokeh effect with soft, out-of-focus circular highlights.
A close-up view of a green leaf with visible holes and imperfections, indicating potential insect damage. The background is blurred, creating a bokeh effect with soft, out-of-focus circular highlights.

Deep Learning

Combining image enhancement and defect classification for accuracy.

A black and white photograph features a close-up of two leaves in the foreground, with visible holes and imperfections. The background is blurred, showing circular bokeh patterns created by sunlight filtering through branches.
A black and white photograph features a close-up of two leaves in the foreground, with visible holes and imperfections. The background is blurred, showing circular bokeh patterns created by sunlight filtering through branches.
PC Crack Dataset

Training lightweight U-Net and ResNet hybrid models effectively.

A monochromatic image features two abstract shapes with a soft focus. The lighting creates a dramatic contrast between light and shadow, enhancing the textures on the surfaces of the objects.
A monochromatic image features two abstract shapes with a soft focus. The lighting creates a dramatic contrast between light and shadow, enhancing the textures on the surfaces of the objects.
The image captures a close-up view of two fabrics side by side. One fabric is black, and the other is a vibrant red, with a noticeable texture visible on both surfaces. The light appears to create a soft gradient, enhancing the contrast between the two colors.
The image captures a close-up view of two fabrics side by side. One fabric is black, and the other is a vibrant red, with a noticeable texture visible on both surfaces. The light appears to create a soft gradient, enhancing the contrast between the two colors.
A digital interface displaying color correction tools with multiple color wheels for adjusting lift, gamma, and gain. The background is dark, emphasizing the brightness and saturation of the color wheels, which are arranged in a row. Each wheel is accompanied by numerical values and sliders for precise adjustments.
A digital interface displaying color correction tools with multiple color wheels for adjusting lift, gamma, and gain. The background is dark, emphasizing the brightness and saturation of the color wheels, which are arranged in a row. Each wheel is accompanied by numerical values and sliders for precise adjustments.
Detection Accuracy

Benchmarking against traditional vision approaches for improved results.