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5.1 KiB

Helmet detection demo

A helmet and head without helmet object detection task demo. Running MobileNet-yolo on K210-based edge devices.


Enviroment preparation

Model generated by aXeleRate and converted to kmodel by nncase.

# master branch for MobileNetv1-yolov2 and unstable branch to test MobileNetv1(v2)-yolov2(v3)
git clone (-b unstable)
cd aXeleRate
pip install -r requirments.txt && pip install -e .

training config setting

Example config, some hyper-parameters:

  • architecture: backbone, MobileNet7_5 for default, MobileNet1_0(α = 1.0) and above cannot run on K210 because of OOM on feature map in master branch. For unstable branch MobileNetV2_1_0 is OK.

  • input_size: fixed model input size, single integer for height equals to width, otherwise a list([height, width]).

  • anchors: yolov2 anchor(for master) or anchor scaled to 1.0(for unstable), can be generate by darknet.

  • labels: labels of all classes.

  • train(valid)_image(annot)_folder: path of images and annoations for training and validation.

  • saved_folder: path for trainig result storage(models, checkpoints, logs ...).

Mine config for unstable:

    "model": {
        "type": "Detector",
        "architecture": "MobileNetV2_1_0",
        "input_size": [
        "anchors": [
        "labels": [
        "obj_thresh": 0.5,
        "iou_thresh": 0.45,
        "coord_scale": 1.0,
        "class_scale": 0.0,
        "object_scale": 5.0,
        "no_object_scale": 3.0
    "weights": {
        "full": "",
        "backend": ""
    "train": {
        "actual_epoch": 2000,
        "train_image_folder": "mydata/human/Images/train",
        "train_annot_folder": "mydata/human/Annotations/train",
        "train_times": 2,
        "valid_image_folder": "mydata/human/Images/val",
        "valid_annot_folder": "mydata/human/Annotations/val",
        "valid_times": 1,
        "valid_metric": "precision",
        "batch_size": 32,
        "learning_rate": 2e-5,
        "saved_folder": "mydata/human/results",
        "first_trainable_layer": "",
        "augmentation": true,
        "is_only_detect": false,
        "validation_freq": 5,
        "quantize": false,
        "class_weights": [1.0]
    "converter": {
        "type": [

(For more detailed config usage, please refer to original aXeleRate repo.)

data preparation

Please refer to VOC format, path as config above.

train it!

python -m aXeleRate.train -c PATH_TO_YOUR_CONFIG

model convert

Please refer to nncase repo.


compile and burn

Use (scons --)menuconfig in bsp folder (Ubiquitous/RT_Thread/bsp/k210), open:

  • More Drivers --> ov2640 driver
  • Board Drivers Config --> Enable LCD on SPI0
  • Board Drivers Config --> Enable SDCARD (spi1(ss0))
  • Board Drivers Config --> Enable DVP(camera)
  • RT-Thread Components --> POSIX layer and C standard library --> Enable pthreads APIs
  • APP_Framework --> Framework --> support knowing framework --> kpu model postprocessing --> yolov2 region layer
  • APP_Framework --> Applications --> knowing app --> enable apps/helmet detect

scons -j(n) to compile and burn in by kflash.

json config and kmodel

Copy json config for deployment o SD card /kmodel. Example config file is helmet.json in this directory. Something to be modified:

  • net_input_size: same as input_size in training config file, but array only.
  • net_output_shape: final feature map size, can be found in nncase output.
  • sensor_output_size: image height and width from camera.
  • kmodel_size: kmodel size shown in file system.
  • anchors: same as anchor in training config file(multi-dimention anchors flatten to 1 dim).
  • labels: same as label in training config file.
  • obj_thresh: array, object threshold of each label.
  • nms_thresh: NMS threshold of boxes.

Copy final kmodel to SD card /kmodel either.


In serial terminal, helmet_detect to start a detection thread, helmet_detect_delete to stop it. Detection results can be found in output.


  • Fix LCD real-time result display.
  • Test more object detection backbone and algorithm(like yolox).