For an electronics-assembly client, YantriX designed a custom inspection camera that flags solder and placement defects on-device. The whole pipeline — sensor, model, firmware, enclosure — fits in a fanless 40 x 40 x 25 mm module.
Overview
A production conveyor inspection camera running a quantized INT8 CNN entirely on an ESP32-S3 — 18 FPS at 0.4 W, no cloud, 6× lower capex per station.
Engagement: Engineering Study
Discipline: Robotics & Automation
Project Type: Edge AI + Custom Hardware
Application: Electronics manufacturing
Key specifications
Objectives
Challenge
The client had a line-side PC-based vision rig that was expensive to scale across many stations and introduced a cloud dependency the plant IT policy didn’t allow. They wanted a self-contained inspection module per station — low power, low cost, zero external dependencies.
Approach
Captured a labelled dataset of nominal and defective assemblies across lighting variants at the client plant.
Designed a compact CNN sized for ESP32-S3 constraints and trained it with aggressive INT8 quantization-aware training.
Designed a custom 4-layer PCB combining ESP32-S3-WROOM, an OV5640 camera module, a local ring light, and MQTT telemetry over Wi-Fi.
Delivered firmware with a ring buffer for failure-case capture so the client can periodically retrain from real edge cases.
Outcomes
Deliverables
Tools used
Impact
Conclusion
The project shows that thoughtfully quantized models plus hardware co-design can put real-time ML into truly constrained devices — not just on single-board computers.
Working on a similar engineering problem?
Need an inspection system that scales across dozens of stations without a PC fleet? We design the device, the model, and the firmware as one thing.
Tagged
Frequently asked questions
Yes — for narrow, well-scoped problems. A 320×320 INT8 CNN with under ~1.5M parameters runs at 15–20 FPS on the ESP32-S3 with ESP-DL acceleration. Defect detection, presence/absence checks, and SKU classification all fit. Object detection across many classes does not.
BOM landed around ₹6,000–12,000 per station versus ₹75,000–1,50,000 for a PC + industrial camera setup. Power per station drops from ~80 W to under 1 W, which compounds over hundreds of stations across a plant.
Related case studies
MLOps · Edge AI Fleet
End-to-end MLOps platform managing 600 Jetson inspection cameras across 14 sites — median model deploy went from 9 days to 38 minutes, with automatic drift-triggered rollback.
Applied AI · Vision-guided robotics
How a YOLOv11-Seg + 3D-pose stack on a Jetson Orin Nano replaced fixed-pose jigs in a 6-DOF robotic cell — sub-80 ms latency, 99.2% accuracy, 40% throughput gain.
Continue exploring
Cross-links help readers move from proof into capability and educational content, and they reinforce the crawl path between commercial pages and reference content.
Discuss your on-device edge vision defect detection on esp32-s3 requirements, design challenges, or target specifications with our engineering team. NDA support is available where required.