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Lane Detection - LaneNet

S100 only

This sample applies only to RDK S100. The RDK S600 system image does not include the corresponding HBM model, and the related sample code is only shipped with the S100 system image; it is not supported on S600 yet.

This sample runs the LaneNet model on the BPU to perform instance segmentation and binary segmentation of lane lines, and saves the result images locally. The sample code is located in /app/cdev_demo/bpu/06_lane_detection_sample/01_lanenet/.

Model Description​

  • Overview:

    LaneNet is a semantic segmentation model for real-time lane detection. LaneNet uses normalization and standardization in image preprocessing, making it suitable for road scene analysis in autonomous driving and ADAS systems. This sample uses the quantized model lanenet256x512.hbm with BPU inference acceleration.

  • HBM model name: lanenet256x512.hbm

  • Input format: RGB, size 256x512, normalized to [0,1] and then standardized

  • Output:

    • instance_seg_logits: 3-channel map for distinguishing different lane lines

    • binary_seg_pred: binary segmentation result indicating lane region locations

Feature Overview​

  • Model loading

    Load the LaneNet model and automatically parse partial model metadata.

  • Input preprocessing

    Convert the input image to RGB format, resize it to 256x512, and apply ImageNet mean and standard deviation for normalization and standardization. Finally convert to NCHW format and add a batch dimension.

  • Inference execution

    Run inference with the .infer() method. Outputs include instance feature maps and binary mask maps.

  • Result post-processing

    Reshape and normalize output tensors:

    • instance_seg_logits: output a three-channel image to visualize each lane instance

    • binary_seg_pred: output a single-channel binary map to extract lane regions

Environment Dependencies​

Before building and running, ensure the following dependencies are installed:

sudo apt update
sudo apt install libgflags-dev

Directory Structure​

.
|-- CMakeLists.txt            # CMake build script: target/dependency/include/link configuration
|-- README.md                 # Usage instructions (this file)
|-- inc
|   `-- lanenet.hpp           # LaneNet inference wrapper header: load/preprocess/infer/postprocess interfaces
`-- src
    |-- lanenet.cc            # LaneNet inference implementation: pre/postprocessing and inference calls
    `-- main.cc               # Program entry: parse args → full pipeline → save instance/binary results

Build the Project​

  • Configure and build 
    mkdir build && cd build
    cmake ..
    make -j$(nproc)

Model Download​

If the model is not found at runtime, download it with the following command:

wget https://archive.d-robotics.cc/downloads/rdk_model_zoo/rdk_s100/Lanenet/lanenet256x512.hbm

Parameter Reference​

ParameterDescriptionDefault Value
--model_pathModel file path (.hbm format)/opt/hobot/model/s100/basic/lanenet256x512.hbm
--test_imgInput test image path/app/res/assets/input.jpg

Quick Start​

  • Run the model

    • Make sure you are in the build directory
    • Use default parameters 
      ./lanenet
    • Run with custom parameters 
      ./lanenet \
          --model_path /opt/hobot/model/s100/basic/lanenet256x512.hbm \
          --test_img   /app/res/assets/input.jpg

  • View the results

    After successful execution, results are drawn and saved to instance_pred.png and binary_pred.png.

    Results saved to: instance_pred.png and binary_pred.png

Notes​

  • The output is saved as instance_pred.png and binary_pred.png for manual inspection.

  • For more deployment options or model support information, refer to the official documentation or contact platform technical support.

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