Development of an Automated Inspection System for Hydraulic Control Unit Hose Assembly Process using Deep Learning-based Object Detection

• In the progress, KCI Paper

1. Project Abstract

Abstract

This study automates quality inspection in hydraulic control device hose assembly using deep learning-based object detection. Human errors in manufacturing contribute significantly to defects, necessitating an automated solution. The proposed system addresses challenges, including distinguishing pre-assembly and assembly completion states, limited detection for small-sized parts, real-time feedback importance, providing hose connection point status, and overcoming blind spots during object detection. The system utilizes YOLOv5 for object detection, introduces a novel assembly state transition algorithm, and employs multi-camera object detection with Python-based multiprocessing. Experimental results showcase successful state transitions, high small object detection rates, and improved performance in detecting assembly completion through multi-camera analysis. This system effectively minimizes errors in the hydraulic control device hose assembly process.

Contributors

Members

Changyeong Kim|Hyungun Cho|Junhyuk Choi

Adviser

Sudong Lee

Contribution

• Changyeong Kim   PM• Model Training• ID-fixing• Switch Wrench Engagement• Multi-Angle detection• Presentation
• Hyungun Cho   K-means based Extracting Hole Center• NGWD
• Junhyuk Choi  Data Preprocessing• Model Training

2. Tool

• Anaconda
• Python3.8
• Pytorch
• Pandas
• Opencv-python
• Pandas
• Numpy
• matplotlib
• scipy

3. Getting Start

Git clone

git clone https://github.com/ChangZero/Multi_Angle_engine_clamp_detection.git

Config

Fill in the multi_angle_detect-config.yaml

cam1:
    detect_path: "" # cam1_detect.py path
    weights: "" # cam1_weights.pt_path
    video_path: "" # cam_video path
    h_info_path: "" # h_info_paht; ex) ./hole_json_file/cam1_h_info.json
    conf-thres: "0.65" # confidence threshold
    epsilon: "100" # Distance from hole to wrench head threshold
    iou: "0.5" # Interaction over Union threshold
    wst: "3" # wrench head stay time

cam2:
    detect_path: ""
    weights: ""
    video_path: ""
    h_info_path: ""
    conf-thres: "0.65"
    epsilon: "100"
    iou: "0.5"
    wst: "3"

Fill in the ./hole_json_file/cam{number}_h_info_json`s hole location infomation

{
    "h1": [0, 0],
    "h2": [0, 0],
    "h3": [0, 0],
    "h4": [0, 0],
    "h5": [0, 0],
    "h6": [0, 0],
    "h7": [0, 0]
}

Build dockerfile

docker build --tag ma-yolo-image .

4. Equipment & Software

• [OS] : Ubuntu 20.04
• [GPU] : CUDA 11.4, NVIDIA RTX A6000
• [Framework] : Pytorch
• [IDE] : Visual Studio Code
• [Collaboration Tool] : Notion, Discord

5. Competitions

• UOU Creative Comprehensive Design Competition(UOU 창의적 종합 설계 경진대회, 2023)
• Engineering FestivalCreative Comprehensive Design Competition(공학페스티벌 창의적 종합 설계 경진대회, 2023)

6. Awards

• Encouragement Prize on Engineering FestivalCreative Comprehensive Design Competition, Ministry of Trade, Industry and Energy(MOTIE), Korea Institute for Advancement of technolohy(KIAT), Research & Information Center for innovation Engineering Education(RICE)
• Grand prize on UOU Creative Comprehensive Design Competition, University of Ulsan Engineering Education Innovation Center

7. License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.