Safety Helmet Detection

Revision History

NO	Version	Description	Date
1	Ver1.0	Initial creation	2026/06/29

The information in this document may be changed without prior notice for the purpose of improving the document. Please refer to our website for the latest version.

Reproduction in any form without the written permission of Nissho Technology Co., Ltd. is strictly prohibited.

1. Safety Helmet DetectionOverview

Safety helmet wearing detection is very important in construction sites, production safety, and security scenarios. Manual visual inspection lacks real-time performance and is difficult to maintain continuously. With the maturity of AI technology, safety helmet wearing detection has become an effective method for supervising helmet use. This safety helmet detection algorithm is an object detection algorithm based on deep learning for detecting and locating persons, and can be effectively used in product implementation.

The performance of this safety helmet detection algorithm on the dataset is as follows:

Safety Helmet Detection Algorithm	mAP@0.5
HELMET	0.93

The execution efficiency on the CSUN RV1126B board is as follows:

Algorithm Type	Execution Efficiency
helmet_detect	68ms

2. Quick Start

2.1 Preparing the Development Environment

If you are reading this document for the first time, refer to Getting Started / Preparing and Updating the Development Compilation Environment, and deploy the compilation environment according to the related steps.

On the Ubuntu system on the PC side, run the run script to enter the Docker development environment. The steps are as follows:

cd ~/linuxshare/work/rv1126b/jp/embedded/images/develop_environment
./run.sh 2204

Figure 2-1 Starting the Docker Development Environment

2.2 Downloading the Source Code

In the Docker development environment, create a management directory for storing the source code repository.

cd /opt/linuxshare/work/rv1126b/jp/AI/demo/ai-algorithm

Use the git tool to clone the remote repository into the management directory.

git clone https://github.com/csunltd/rv1126b-ai-toolkit.git

※ Even when downloading from the GitHub web page, download the entire repository. Do not download only the directory corresponding to this sample.

※ If the repository has already been downloaded, skip this step.

2.3 Model Deployment

To run the algorithm Demo, first download the safety helmet detection algorithm model.

Download link:

https://dl.dragonwake.com/download/rv1126b/AI/demo/06_helmet_detect.zip

Then copy the downloaded safety helmet detection algorithm model to the Release/ directory.

Figure 2-2 Files in the Release Directory

2.4 Building the Sample

Move to the directory containing the sample and execute the build. The specific commands are as follows:

cd rv1126b-ai-toolkit/Demos/algorithm-helmet/

./build.sh cpres

* Because the dependent libraries are located on the development board, keep /mnt mounted during cross-compilation.

sudo mount -t nfs -o vers=3,proto=tcp,mountproto=tcp,nolock,retrans=5,timeo=5 192.168.10.85:/ /mnt

* When the cpres parameter is specified for the build.sh script, all resources in the Release/ directory are copied to the development board.

Figure 2-3 Sample Build Result

2.5 Sample Execution and Results

Copy the compiled files to the board.

cp Release/\* /mnt/userdata/Demo/algorithm-helmet/

Enter the backend of the development board through serial debugging or SSH debugging, and move to the sample deployment directory as follows:

cd /userdata/Demo/algorithm-helmet/

The command for running the sample is as follows:

./test-helmet_detect helmet_detect.model test.jpg

Figure 2-4 Sample Execution Result

In the Docker development environment, copy the test Result Image from the board.

cp /mnt/userdata/Demo/algorithm-helmet/result.jpg .

Figure 2-5 Command for Copying the Result Image

The recognition result is shown in the following figure.

Figure 2-6 Safety Helmet Detection Result Image

For detailed API descriptions and API calls (the source code of this sample), refer to the following.

3. Safety Helmet DetectionAPI Description

3.1 Reference Method

To allow users to call the EASY EAI API library directly from a local project, the libraries and header files that need to be linked in this project are listed below. Users can add them directly.

Item	Description
Header File Directory	easyeai-api/algorithm/helmet_detect
Library File Directory	easyeai-api/algorithm/helmet_detect
Library Link Parameter	-lhelmet_detect

3.2 Safety Helmet DetectionInitialization Function

The prototype of the safety helmet detection initialization function is as follows.

int helmet_detect_init(rknn_context \*ctx, const char \* path)

The details are as follows.

Function Name: helmet_detect_init()
Header File	helmet_detect.h
Input Parameter	ctx: rknn_context handle
Input Parameter	path:algorithm model path
Return Value	Return value on success: 0
	Return value on failure: -1
Notes	None

3.3 Safety Helmet DetectionExecution Function

The prototype of the safety helmet detection execution function helmet_detect_run is as follows.

Function Name:helmet_detect_run()
Header File	helmet_detect.h
Input Parameter	ctx: rknn_context handle
Input Parameter	input_image:image data input(cv::Mat is an OpenCV type)
Output Parameter	output_dets:object detection box output
Return Value	Return value on success: 0
	Return value on failure: -1
Notes	None

3.4 Safety Helmet DetectionRelease Function

The prototype of the safety helmet detection release function is as follows.

int helmet_detect_release(rknn_context ctx)

The details are as follows.

Function Name:helmet_detect_release ()
Header File	helmet_detect.h
Input Parameter	ctx: rknn_context handle
Return Value	Return value on success: 0
	Return value on failure: -1
Notes	None

4. Safety Helmet Detection Algorithm Sample

The sample directory is Demos/algorithm-helmet/test-helmet_detect.cpp, and the operation flow is as follows.

Figure 4-1

The reference sample is as follows.

#include <opencv2/opencv.hpp>
#include <stdio.h>
#include <sys/time.h>
#include"helmet_detect.h"

using namespace cv;
using namespace std;

static Scalar colorArray[10]={
    Scalar(255, 0, 0, 255),
    Scalar(0, 255, 0, 255),
    Scalar(0,0,139,255),
    Scalar(0,100,0,255),
    Scalar(139,139,0,255),
    Scalar(209,206,0,255),
    Scalar(0,127,255,255),
    Scalar(139,61,72,255),
    Scalar(0,255,0,255),
    Scalar(255,0,0,255),
};

int plot_one_box(Mat src, int x1, int x2, int y1, int y2, char *label, char colour)
{
    int tl = round(0.002 * (src.rows + src.cols) / 2) + 1;
    rectangle(src, cv::Point(x1, y1), cv::Point(x2, y2), colorArray[(unsigned char)colour], 3);

    int tf = max(tl -1, 1);

    int base_line = 0;
    cv::Size t_size = getTextSize(label, FONT_HERSHEY_SIMPLEX, (float)tl/3, tf, &base_line);
    int x3 = x1 + t_size.width;
    int y3 = y1 - t_size.height - 3;

    rectangle(src, cv::Point(x1, y1), cv::Point(x3, y3), colorArray[(unsigned char)colour], -1);
    putText(src, label, cv::Point(x1, y1 - 2), FONT_HERSHEY_SIMPLEX, (float)tl/3, cv::Scalar(255, 255, 255, 255), tf, 8);
    return 0;
}

int main(int argc, char **argv)
{
  if (argc != 3)
    {
        printf("%s <model_path> <image_path>\n", argv[0]);
        return -1;
    }

    const char *model_path = argv[1];
    const char *image_path = argv[2];

  /* パラメータ初期化 */
  detect_result_group_t detect_result_group;

  /* アルゴリズムモデル初期化 */
  rknn_context ctx;
  helmet_detect_init(&ctx, model_path);

  /* アルゴリズム実行 */
  cv::Mat src;
  src = cv::imread(image_path, 1);

  struct timeval start;
  struct timeval end;
  float time_use=0;

  gettimeofday(&start,NULL);

  helmet_detect_run(ctx, src, &detect_result_group);

  gettimeofday(&end,NULL);
  time_use=(end.tv_sec-start.tv_sec)*1000000+(end.tv_usec-start.tv_usec);//マイクロ秒
  printf("time_use is %f\n",time_use/1000);

  /* アルゴリズム結果を画像上に描画して保存します */
  // Draw Objects
  char text[256];
  for (int i = 0; i < detect_result_group.count; i++)
  {

    detect_result_t* det_result = &(detect_result_group.results[i]);
    if( det_result->prop < 0.4)
    {
      continue;
    }

    sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100);
    printf("%s @ (%d %d %d %d) %f\n", det_result->name, det_result->box.left, det_result->box.top,
         det_result->box.right, det_result->box.bottom, det_result->prop);
    int x1 = det_result->box.left;
    int y1 = det_result->box.top;
    int x2 = det_result->box.right;
    int y2 = det_result->box.bottom;
    /*
    rectangle(src, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(255, 0, 0, 255), 3);
    putText(src, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
    */
    plot_one_box(src, x1, x2, y1, y2, text, i%10);
  }

  cv::imwrite("result.jpg", src);


  /* アルゴリズムモデルのリソースを解放します */
  helmet_detect_release(ctx);

  return 0;
}