Person Detection ML Application

Description

The UC Person Detection application is designed to identify and locate persons within its field of view. It leverages object detection techniques to generate bounding boxes around detected individuals and assigns confidence scores to indicate the reliability of each detection. The output includes the precise location of each person in the image along with a confidence value, enabling accurate and efficient person recognition for various embedded vision applications. This example supports both WQVGA(480x270) and VGA(640x480) resolutions.

Prerequisites

Choose one setup path:
- CLI: Setup and Install SDK using CLI
- VS Code: Setup and Install SDK using VS Code

Building and Flashing the Example using VS Code

Use the VS Code flow described in the SR110 guide and the VS Code Extension guide:

Build (VS Code):

Open Build and Deploy → Build Configurations.
Select person_detection in the Application dropdown.
If you need VGA (640x480), click Edit Configs (Menuconfig) in the Build and Deploy view, then set
COMPONENTS CONFIGURATION → Off Chip Components → Display Resolution to VGA.
Optional configuration changes in Menuconfig:
- WQVGA in LP Sense: COMPONENTS CONFIGURATION → Drivers → enable MODULE_LP_SENSE_ENABLED
- Static Image: COMPONENTS CONFIGURATION → Off Chip Components → disable MODULE_IMAGE_SENSOR_ENABLED
Build with Build (SDK + App) for the first build, or Build App for rebuilds.

Flash (VS Code):

Use Image Conversion to generate the flash image.
Use Image Flashing (SWD/JTAG) to flash the firmware image.
VGA use case: flash the model binary second, after the use case image.
In Image Flashing, check Model Binary and set Flash Offset to 0x629000, then flash the model file.
After that, flash the firmware image normally.

Building and Flashing the Example using CLI

Use the CLI flow described in the SR110 guide:

SR110 Build and Flash with CLI

Build (CLI):

From <sdk-root>/examples, build the example:

cd <sdk-root>/examples
export SRSDK_DIR=<sdk-root>
make cm55_person_detection_defconfig BOARD=SR110_RDK BUILD=SRSDK

If you need VGA (640x480), open Kconfig and set
COMPONENTS CONFIGURATION → Off Chip Components → Display Resolution to VGA:
```
make cm55_person_detection_defconfig BOARD=SR110_RDK BUILD=SRSDK EDIT=1
```
Optional configuration changes in Menuconfig:
- WQVGA in LP Sense: COMPONENTS CONFIGURATION → Drivers → enable MODULE_LP_SENSE_ENABLED
- Static Image: COMPONENTS CONFIGURATION → Off Chip Components → disable MODULE_IMAGE_SENSOR_ENABLED

Flash (CLI):

Activate the SDK venv (required for image generation tools):

# Linux/macOS
source <sdk-root>/.venv/bin/activate
# Windows PowerShell
.\.venv\Scripts\Activate.ps1

Generate the flash image:

cd <sdk-root>/tools/srsdk_image_generator
python srsdk_image_generator.py \
  -B0 \
  -flash_image \
  -sdk_secured \
  -spk "<sdk-root>/tools/srsdk_image_generator/B0_Input_examples/spk_rc4_1_0_secure_otpk.bin" \
  -apbl "<sdk-root>/tools/srsdk_image_generator/B0_Input_examples/sr100_b0_bootloader_ver_0x012F_ASIC.axf" \
  -m55_image "<sdk-root>/examples/out/sr110_cm55_fw/release/sr110_cm55_fw.elf" \
  -flash_type "GD25LE128" \
  -flash_freq "67"

Flash the image:

cd <sdk-root>
python tools/openocd/scripts/flash_xspi_tcl.py \
  --cfg_path tools/openocd/configs/sr110_m55.cfg \
  --image tools/srsdk_image_generator/Output/B0_Flash/B0_flash_full_image_GD25LE128_67Mhz_secured.bin \
  --erase-all

VGA use case: flash the model binary second at offset 0x629000:

cd <sdk-root>
python tools/openocd/scripts/flash_xspi_tcl.py \
  --cfg_path tools/openocd/configs/sr110_m55.cfg \
  --image <path-to-model-bin> \
  --flash-offset 0x629000

Running the Application using VS Code Extension

Windows note: Ensure the USB drivers are installed for streaming. See the Zadig steps in
SR110 Build and Flash with VS Code.

In VS Code, open Video Streamer from the Synaptics sidebar.
For logging output, click SERIAL MONITOR and connect to the DAP logger port on J14.
- To make it easier to identify, ensure only J14 is plugged in (not J13).
- The logger port is not guaranteed to be consistent across OSes. As a starting point:
  - Windows: try the lower‑numbered J14 COM port first.
  - Linux/macOS: try the higher‑numbered J14 port first.
- If you don’t see logs after a reset, switch to the other J14 port.
In the Video Streamer dropdown, select the J13 COM port.
- Plug in J13 and press RESET on the board.
- Windows: select the newly enumerated COM port.
- Linux/macOS: select the lower‑numbered COM port of the two newly enumerated ports.
Use the Video Streamer controls:

a. Select PERSON_DETECTION from the UC ID dropdown.
b. Set RGB Demosaic to BayerRGGB.
c. Click Create Use Case.
d. Click Start Use Case (a Python window opens and the video stream appears).
Autorun use cases: If autorun is enabled, after step 4 click Connect Image Source to open the video stream pop-up.

Adapting Pipeline for Custom Object Detection Models

This person detection pipeline can be adapted to work with custom object detection models. However, certain validation steps and potential modifications are required to ensure compatibility.

Prerequisites for Model Compatibility

Before adapting this pipeline for another object detection model, you must verify the following:

1. Model Format Requirements

Your object detection model should be in .tflite format
The model should produce similar output tensor structure (bounding boxes, confidence scores)

2. Vela Compiler Compatibility Check

Step 1: Analyze Original Model

Load your object_detection_model.tflite file in Netron
Document the output tensors:
- Tensor names
- Tensor identifiers/indexes
- Quantization parameters (scale and offset values)
- Tensor dimensions

Step 2: Compile with Vela

Pass your model through the Vela compiler to generate model_vela.bin or model_vela.tflite
Analyze the Vela-compiled model in Netron using the same steps as above

Step 3: Compare Outputs Compare the following between original and Vela-compiled models:

Output tensor indexes/identifiers: Verify if they remain in the same order
Quantization parameters: Check if scale and offset values are preserved
Tensor dimensions: Ensure dimensions match your expected output format

Pipeline Adaptation Process

Case 1: No Changes Required

If the Vela compilation preserves:

✅ Output tensor indexes in the same order
✅ Same quantization scale and offset values

Result: You can proceed with the existing pipeline without modifications.

Case 2: Modifications Required

If the Vela compilation changes:

❌ Output tensor index order
❌ Quantization parameters

Required Actions: Modify the pipeline code as described below.

Code Modifications

If your model’s output tensor indexes change after Vela compilation, you need to update the tensor parameter assignments in uc_person_detection.c:

Location: `detection_post_process` function

Original Code:

g_box1_params = &g_all_tens_params[0];
g_box2_params = &g_all_tens_params[1];
g_cls_params  = &g_all_tens_params[2];

Modified Code: Update the array indexes according to your Vela-compiled model’s output tensor identifiers:

// Example: If your model_vela output has different tensor order
g_box1_params = &g_all_tens_params[X];  // Replace X with actual index from Netron
g_box2_params = &g_all_tens_params[Y];  // Replace Y with actual index from Netron
g_cls_params  = &g_all_tens_params[Z];  // Replace Z with actual index from Netron