There is a matching video for this article where I walk through the hardware and the code step by step. If you haven’t read the first post of this series, check it out first as it covers the basics.
The CrowPanel Advanced 7 inch display is one of those boards that immediately raises eyebrows when you see what it can do for the price. It is a standalone touchscreen, a camera, and real time face detection running locally, with no internet connection and no external computer. Everything happens on the board itself.
In this deep dive article, I focus on what is actually happening behind the scenes. Not just what the board can do, but how Elecrow’s example code is structured and how face detection and pedestrian detection work on an ESP32 P4.
Hardware Overview and Core Capabilities
At the heart of the CrowPanel Advanced is the ESP32 P4 microcontroller, running at up to 400 MHz and designed with machine learning acceleration in mind. This is not a general purpose CPU pretending to do AI. The P4 includes features that make it practical to run small neural network models efficiently, without active cooling or large power budgets .
The board pairs the P4 with a 7 inch IPS touchscreen and a 2 megapixel camera module connected directly to the processor. Camera frames are captured, processed, and analyzed entirely on device. There is no cloud backend, no video streaming to external servers, and no subscription service involved. For applications where privacy, latency, or offline operation matters, this architecture is significant.
The face detection model used in Elecrow’s demo is around 100 KB in size. That constraint drives many of the design tradeoffs you see in performance and accuracy, but it is also what makes real time inference possible on such modest hardware.
Face Detection in Practice
When running the face detection demo from Elecrow’s GitHub repository, the behavior is immediately intuitive. The software draws bounding boxes around detected faces and updates them smoothly as the subject moves closer or farther from the camera. Box size scales with distance, and multiple faces can be detected simultaneously without visible lag .
Detection does have limits. If you move too close, too far away, or at extreme angles, the model may fail to detect a face. This is expected. Small models are trained on limited datasets and optimized for speed and size rather than edge case robustness. Within good lighting and typical frontal views, however, the results are stable and responsive.
What matters here is not that the detection is perfect, but that it is fast, local, and predictable. For many embedded applications, those qualities are more valuable than absolute accuracy.
Pedestrian Detection and Broader Use Cases
The pedestrian detection mode uses a different model and detection strategy. Instead of focusing on facial features, it detects full human figures. The bounding box shifts from framing the face to covering the body, which immediately changes how you might use it.
This type of detection is useful in scenarios like doorway monitoring, people counting, or presence triggered actions. You do not need identity recognition to know that someone entered a space. You only need reliable detection of a person entering or leaving the frame .
The example makes it easy to see how such a system could trigger recording, increment counters, or activate other logic when motion corresponds to a human presence rather than random movement.
More Than AI Demos
Elecrow’s example firmware is not limited to artificial intelligence demos. It is a collection of small applications designed to show the full range of the hardware.
One of the most instructive examples is the calculator app. It does not use AI at all, but it demonstrates touchscreen handling, graphics rendering, animations, and responsiveness. Buttons are laid out in code as a simple array, defining their labels and order. Touch events are mapped to handler functions that update the display and perform calculations .
This app shows that the CrowPanel is capable of running complete graphical user interfaces with smooth animations and accurate touch input. From an embedded development perspective, this is just as important as the AI features.
Project Structure and Code Organization
The example code is written in C++ and organized in a way that is typical for larger embedded projects. The main application file acts as the entry point. When the board powers up, this file initializes storage, configures the display and graphics system, enables the backlight, and constructs the phone style launcher interface .
Applications are installed one by one into this interface. Calculator, music player, settings, simple games, and camera based apps are all registered during startup. Once this setup phase is complete, control is handed over to the menu system. There is no single infinite loop in the main file dictating behavior. Instead, each app responds to events as the user interacts with it.
Supporting code lives in component folders. These handle hardware initialization, pin mappings, camera configuration, and display drivers. Application developers rarely need to modify these unless they are changing hardware behavior.
Inside the Face Detection Component
The face detection logic lives in its own component folder. This separation makes the code easier to understand and reuse. One subfolder contains the trained model files. These represent the result of extensive offline training, where thousands of labeled images were used to teach a neural network what constitutes a face .
At runtime, the model is loaded into memory and paired with an image preprocessing pipeline. Each camera frame is resized, color adjusted, and normalized before being passed into the model. Confidence thresholds determine how certain the model must be before reporting a detection. Lower thresholds increase sensitivity but also false positives. Higher thresholds reduce noise but may miss valid detections.
After inference, the code applies non maximum suppression. In practical terms, this removes overlapping bounding boxes that refer to the same face. The output is a clean list of detected faces and their positions, which other parts of the program use to draw boxes on the display.
The entire detection pipeline is compact, focused, and readable. It does one job and does it well.
Limits and Realistic Expectations
It is important to keep expectations grounded. This board is not intended to replace a desktop GPU or a high end vision system. The models are small by necessity, and they perform best in controlled conditions. Poor lighting, unusual angles, or highly specific object detection tasks will expose the limits quickly .
Training custom models for specialized tasks is possible in theory, but it is a nontrivial undertaking. Dataset creation, training pipelines, and model optimization are projects in their own right. For general face and person detection, however, the provided demos already cover a wide range of practical applications.
Development Environment and Getting Started
To build and flash the example code, you need the ESP IDF development framework version 5.4 or newer. Once set up, the project builds cleanly and runs without modification. All source code is publicly available on Elecrow’s GitHub repository, making this board approachable both as a learning platform and as a base for custom projects .
The CrowPanel Advanced shows what is now possible at the low end of embedded hardware. Real time computer vision, responsive graphical interfaces, and standalone operation are no longer exclusive to expensive platforms. In the next articles and videos, I will build on this foundation and start modifying the examples to create more practical, application driven behavior.
Useful Resources
Below is a curated list of resources to help you making the most of the CrowPanel Advanced P4 and ESP32-P4.
- CrowPanel Advanced 7 Inch ESP32 P4 Product Page
- CrowPanel Advanced ESP32 P4 Official GitHub Repository
- ESP32 P4 Datasheet
- Elecrow Advanced HMI ESP32 P4 Course PDF
- ESP IDF Development Framework Documentation
- ESP DL AI Framework (Espressif)
- LVGL Graphics Library
- ESP32 Camera Driver Documentation
- Elecrow Documentation and Downloads