AI & Computer Vision

Exploring Edge-Ready Object Detection with YOLON & YOLOL

Over the past few months I’ve delved deep into real-world object detection using the ultralytics YOLO family, particularly two lightweight versions: YOLON (YOLO Weight Version Nano) and YOLOL (YOLO Weight Version Light).

Why Lightweight Models?

When hardware or latency is limited, full-scale models can choke.

YOLON (Nano) is built for edge devices with minimal resources while still delivering usable detection performance.
YOLOL (Light) gives a balance: higher accuracy than Nano, but still efficient enough for deployment in many real-time setups.

My Workflow & Achievements

Using CVZone as a wrapper around Ultralytics models, I built:

A custom pipeline to take video or camera feed and detect vehicles, pedestrians, trucks, and non-standard objects.
A workflow to switch between YOLON and YOLOL dynamically — YOLON for low-resource/quick inference, YOLOL for higher-accuracy scenarios.
Extensive testing on urban traffic scenarios: pinpointing cars, trucks, movement patterns, and supporting analytics. The detection samples shown here reflect real-world experimentation on a live street scene.

Key Learnings

Choosing the correct model matters: when hardware is constrained, YOLON delivers efficient inference; when latency and accuracy can trade-off, YOLOL is the better fit.
CVZone simplified model integration, bounding box drawing, label handling and live-feed visualization — enabling faster prototyping and deployment.
Real-world data brings surprises: lighting changes, occlusion, varied object sizes. A model trained well in lab may still face edge cases — so real-world testing is crucial.

What’s Next?

Fine-tuning YOLOL on custom datasets (e.g., local traffic patterns, specific vehicle types) to improve accuracy further.
Exploring hybrid mode: detect with YOLON, then if uncertain escalate to YOLOL for refinement.
Integrating alerts or analytics that act on detection events (for example: unauthorized vehicles, crowd monitoring, traffic anomaly detection).

If you’re interested in computer vision solutions — whether object detection on edge devices, camera analytics, or integrating YOLO models into your workflow — feel free to reach out. I’d be happy to share more of my findings, demos, or even work together.

Let’s bring vision to reality!

— Aakash Pradhan

Cybersecurity & IT Specialist | Web & CV Systems

[Contact me for collaboration]