Computer vision models can deliver impressive results in controlled environments. Turning them into reliable embedded products is mostly a matter of engineering the camera and the hardware around the model. Yalantis is a compliance-first, full-cycle tech partner with deep expertise in IoT and computer vision, covering software, embedded, hardware and AI development. This article draws on four Yalantis projects to show how that work is done in practice - from optics and image correction to power design and field validation. Why vision models that work in the lab struggle on real devices A computer vision model that performs well in a lab often struggles on a real device because the lab hides the problems a physical system creates. In the lab, the images are consistent and the lighting is controlled. There's usually enough processing power to run inference without worrying about the rest of the system. Then the model moves into the real world. On a factory floor, reflections from metal surfaces can look like defects, and parts on a conveyor leave only a short window for inspection. These problems are easy to underestimate during model development. At Yalantis, we've run into them across very different embedded computer vision projects: inspecting automotive electronics, detecting fractures in brake calipers, monitoring stock inside commercial refrigerators and analyzing microplastic particles in flowing water. The constraints differed from project to project. Some systems had to keep pace with a fast production line. Others had to run for years on batteries or produce measurements that were hard to validate at all. All four projects led to the same realization: a reliable vision system depends on engineering everything around the model, from the first image the camera captures to the decision the device delivers. That work often starts at the camera. Why fixing the image comes before changing the model Many vision errors start before the image ever reaches the model, so the camera and image processing are the first places to look. When a vision system starts making mistakes, it's tempting to blame the model and reach for more training data or a bigger network. We saw this on an automotive electronics line. The manufacturer used automated optical inspection (AOI) to check printed circuit boards for engine and airbag controllers, and the AOI was flagging too many good boards. Most of the trouble came from lead-free solder joints. Their uneven, reflective surfaces created patterns the AOI struggled to tell apart from real defects, so operators spent hours each shift re-checking boards that were often fine. Replacing the AOI equipment would have been expensive, so we developed a secondary inspection system to examine the boards the original machine flagged. Before selecting a model, our engineers spent two weeks on the factory floor analyzing flagged images. Most false alarms turned out to come from changing lighting and solder reflections, so the images had to be normalized before inference. A similar problem appeared in another automotive project, this time with cast aluminum brake calipers . The manufacturer needed to detect micro-fractures in polished metal parts, and a generic vision system had already failed because reflections and shifting ambient light hid the cracks. The team started with how the images were captured. Multi-angle dome lighting with polarizing filters suppressed specular reflections, and high-resolution monochrome sensors with telecentric lenses produced consistent images without perspective distortion. With the optics in place, a custom U-Net segmentation model running on NVIDIA Jetson Orin could outline potential cracks. Real examples of rare defects were scarce, so the training set was extended with synthetic images. The two projects reached the same goal with different techniques. The PCB system fixed image consistency in preprocessing, while the caliper station fixed it with lighting and optics. Both ap