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Object Detection - Ultralytics YOLO11

This example is based on the Ultralytics YOLO11 model and performs object detection on images using C/C++. It supports image preprocessing, inference, post-processing (including decoding, confidence filtering, and NMS), and saving the result image. The example code is located in the /app/cdev_demo/bpu/02_detection_sample/02_ultralytics_yolo11/ directory.

Model Description​

  • Overview:

    Ultralytics YOLO11 is a lightweight anchor-based object detection model that integrates both anchor-free and anchor-based concepts, offering fast inference and precise localization. During the regression phase, this model employs discrete regression bins combined with softmax classification and a decoding mechanism to enhance localization accuracy. Ultralytics YOLO11 is suitable for deploying small models in real-time scenarios, such as security surveillance and industrial inspection.

  • HBM Model Name: yolo11n_detect_nashe_640x640_nv12.hbm

  • Input Format: NV12 format, sized 640x640 (separated Y and UV planes)

  • Output: Multi-scale feature maps, each containing a class score tensor and a discrete bounding box regression tensor. The final output includes bounding box coordinates, class IDs, and confidence scores.

Functionality Description​

  • Model Loading

    Load the quantized Ultralytics YOLO11 model and extract model metadata for subsequent inference steps.

  • Input Preprocessing

    Resize the original BGR image to 640×640, convert it to NV12 format (separated Y and UV planes), and construct a nested input tensor dictionary compatible with the inference interface.

  • Inference Execution

    Run forward inference using the .infer() method, producing classification and regression tensors from multiple scale branches.

  • Result Post-processing

    • Dequantize the quantized output back to float32;
    • Filter classification scores for each scale branch, retaining candidate boxes with confidence scores above a specified threshold;
    • Decode bounding boxes using a multi-bin regression algorithm;
    • Merge candidate boxes from all scales and apply NMS (Non-Maximum Suppression) to remove redundant boxes;
    • Map detected boxes from the model input coordinate system back to the original image dimensions;
    • Optionally draw detection results and save the output image file.

Environment Dependencies​

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

sudo apt update
sudo apt install libgflags-dev

Directory Structure​

.
├── CMakeLists.txt               # CMake build script
├── README.md                    # Usage instructions
├── inc
│   └── ultralytics_yolo11.hpp   # YOLO11 model wrapper header file
└── src
    ├── main.cc                  # Main program entry point
    └── ultralytics_yolo11.cc    # YOLO11 model implementation

Building the Project​

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

Model Download​

If the model is not found during program execution, download it using the following command:

wget https://archive.d-robotics.cc/downloads/rdk_model_zoo/rdk_s100/ultralytics_YOLO/yolo11n_detect_nashe_640x640_nv12.hbm

Parameter Description​

ParameterDescriptionDefault Value
--model_pathPath to the model file (.hbm format)/opt/hobot/model/s100/basic/yolo11n_detect_nashe_640x640_nv12.hbm
--test_imgPath to the input test image/app/res/assets/kite.jpg
--label_filePath to the class label file (one class name per line)/app/res/labels/coco_classes.names
--score_thresConfidence threshold for filtering (objects below this value will be discarded)0.25
--nms_thresIoU threshold for Non-Maximum Suppression (NMS)0.7

Quick Start​

  • Running the Model

    • Using default parameters: 
      ./ultralytics_yolo11
    • Running with custom parameters: 
      ./ultralytics_yolo11 \
          --model_path /opt/hobot/model/s100/basic/yolo11n_detect_nashe_640x640_nv12.hbm \
          --test_img /app/res/assets/kite.jpg \
          --label_file /app/res/labels/coco_classes.names \
          --score_thres 0.25 \
          --nms_thres 0.7

  • Viewing Results

    Upon successful execution, bounding boxes will be drawn on the original image and saved as build/result.jpg:

    [Saved] Result saved to: result.jpg

Notes​

  • The output result is saved as result.jpg, which users can inspect directly.

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

License​

Copyright (C) 2025, XiangshunZhao D-Robotics.

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.
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