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.

ChangZero / Multi_Angle_YOLOv5