“No helmet, no green” is a maker-built AI traffic-light demonstration—not a live road-control system. Created by Roni Bandini, it uses a camera and an edge-AI model to classify a motorcycle rider as wearing a helmet or not, then displays a corresponding signal at a scaled Lego intersection.
What “no helmet, no green” means
Bandini’s project, published on Hackster.io on November 2, 2023, adapts a concept the creator attributes to Honda Safety in Argentina: use cameras to detect whether motorcycle riders are wearing helmets and withhold a green indication when a rider is classified as helmetless. The documented build demonstrates that idea with Lego figures and a miniature intersection; it does not establish that the system is deployed or validated on public roads. Read the project walkthrough.
How the prototype works
The system divides image processing from signal display. A USB camera feeds images to a Texas Instruments AM62A board, which runs the trained model and sends an inference value to an intermediate server. A DFRobot UNIHIKER board runs a Python script that queries the server and displays a traffic-light state. Bandini says an ESP32 with LEDs could be used instead of the UNIHIKER display arrangement; the project does not report a comparative test between them.
- Capture: A USB camera supplies an image of the Lego rider to the AM62A.
- Classify: The model identifies one of two labels, “helmet” or “nohelmet.”
- Pass the result: The AM62A sends an inference value to a server.
- Show a state: The UNIHIKER script queries the server and controls the miniature traffic-light display.
The creator also made a small 3D-printed base for the model traffic light. The public Edge Impulse page describes the project as an “AI traffic light system to detect motorcycle helmets.” See the public Edge Impulse project page.
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What you need to reproduce the documented setup
| Part | Role in the project |
|---|---|
| TI AM62A board | Runs image inference; the Edge Impulse project identifies the target as TI AM62A with a deep-learning accelerator. |
| USB camera | Captures images for the model. The walkthrough does not establish a required camera model. |
| Intermediate server | Receives an inference value from the AM62A and is queried by the display script. |
| DFRobot UNIHIKER board | Runs the Python script that queries the server and displays signal states. |
| Traffic-light display and miniature scene | Shows the result at the scaled Lego intersection; the project also describes a 3D-printed light base. |
Bandini names an ESP32 with LEDs as an alternative for the display arrangement, not as a replacement for the AM62A inference board. The article does not establish that a particular camera, server configuration, or mounting position is necessary or sufficient for real-world use. The Hackster walkthrough lists the project components and wiring approach.
How the helmet model was trained
The creator describes configuring image object detection in Edge Impulse Studio with two labels: “helmet” and “nohelmet.” The tutorial’s training example uses 60 photographs of a Lego figure with and without a helmet, resized to 96 × 96 pixels, with 60 training cycles, a learning rate of 0.001, and data augmentation. The public Edge Impulse page lists 116 data items. These are details of this project’s data and model setup, not evidence that the model has been trained on varied road users or conditions.
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What the reported performance numbers do—and do not—show
Bandini’s 2023 walkthrough reports a 95.2% F1 score for the Lego-based model. The undated public Edge Impulse page, accessed October 4, 2026, displays a 95.2% validation result, a 95.0% test result, and 3 ms on-device latency for TI AM62A using an unoptimized float32 model. These are project-page metrics tied to its data and configuration; F1 is not the same measure as accuracy. Edge Impulse’s public project page shows its current displayed metrics.
Those figures do not establish how often the detector would correctly classify riders in live traffic. The documented evaluation does not demonstrate performance across real road footage, changing camera angles, lighting, helmets, riders, or operating conditions. Nor does an inference latency figure establish that an end-to-end traffic signal would respond safely or reliably; the camera, server, communications, and signal-control behavior also matter.
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Why it is not ready to control a real intersection
The project is a miniature demonstration, and its server example handles only one inference value. Bandini notes that serving multiple traffic lights would require a traffic-light identifier and additional security layers. A real deployment would also need an independently validated detection system and a carefully designed fail-safe approach so a missed detection, network interruption, or software fault cannot create an unsafe signal state. The project walkthrough does not document such validation or a road authority’s approval.
Alerts and license-plate OCR are mentioned as possible extensions, not demonstrated features. The prototype is not shown automatically issuing tickets. Its setup article also contains historical installation commands and example credentials; anyone adapting the code should consult current vendor documentation and configure secure credentials and server access rather than reuse examples uncritically.
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