Teaching machines to see

01 / The situation

Before the agents, there were pixels: surveillance, robotics, manufacturing and e-commerce problems that needed models fast and accurate enough to actually deploy.

This collection spans YOLO object detection, stereo and monocular dimension measurement, monocular depth estimation and a pix2pix image-to-image model.

What made it an integration problem

The CV describes real-time detection and resource-constrained depth estimation, alongside manufacturing and quality-control measurement applications.

02 / My contribution

The parts I built.

1. 01

   Built an end-to-end YOLO object-detection pipeline optimized to run at 20 FPS for surveillance and autonomous navigation.

2. 02

   Engineered a stereo + monocular computer-vision dimension-measuring machine, improving measurement precision by 20%.

3. 03

   Developed a cGAN (pix2pix) image-to-image translation model for editing, style transfer and colorization — a 10% lift in generated realism.

4. 04

   Created a real-time monocular depth-estimation model with a 25% reduction in compute overhead for resource-constrained deployment.

03 / The system

Follow the information.

1. 01Camera / image input↓
2. 02Model inference↓
3. 03Detection / depth / measurement↓
4. 04Application integration

Conceptual workflow reconstructed from the CV. This is not a screenshot of a client system.

04 / An engineering reading

Why the connections matter.

The work includes both models and the pipelines around them: inference speed for detection, 3D point-cloud generation for measurement, and integration for depth estimation.

The tradeoff to pay attention to

Speed, compute cost and output quality need to be read together. The CV provides relative improvements but not hardware, datasets or evaluation protocols. Those measurements should not be treated as portable performance guarantees.

This section interprets the delivered scope. It does not claim an undocumented decision process.