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DasKunststoffZentrumSKZ
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Errors_Additive_Manufacturing_Nozzle_Cam

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Object Detection
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Errors_Additive_Manufacturing_Nozzle_Cam

3D Printing Nozzle Camera – YOLO Object Detection Dataset

This Repository is part of the Project: Künstliche Intelligenz zur Automatiserten Fehlerkorrektur in der Additiven Fertigung(Förderkennzeichen: 16IS23050B).

This dataset contains images captured from a camera positioned directly next to the nozzle of a 3D printer. The task is object detection of both regular print elements and typical printing defects.

The dataset is provided in standard YOLO format and can be used directly with Ultralytics YOLO.

Content

The nozzle-near camera captures:

  • Nozzle
  • Printed Object
  • Purge Line
  • Typical 3D printing defects

Classes

  1. Nozzle
  2. Object
  3. Purge Line
  4. Spaghetti
  5. Stringing
  6. Unterextrusion
  7. Warping
  8. schlechte erste Schicht
  9. Double Print

Number of classes: 9

Structure 


Errors_Additive_Manufacturing_Nozzle_Cam/
│
├── images/
│   ├── train/
│   ├── val/
│   └── test/
│
├── labels/
│   ├── train/
│   ├── val/
│   └── test/
│
├── dataset.yaml
├── summary_classes.csv
└── summary_splits.csv

YOLO Label Format

Each image has a corresponding .txt annotation file.

Format per row 


<class_id> <x_center> <y_center> <width> <height>

Details

  • Coordinates are normalized to [0,1]
  • class_id corresponds to the class index defined in dataset.yaml
  • Multiple objects are stored as multiple rows in one file

Example 


0 0.512 0.423 0.120 0.085
3 0.621 0.558 0.210 0.175

Training with Ultralytics YOLO

Example training script:

from ultralytics import YOLO

model = YOLO("yolov8n.pt")  # or your pretrained checkpoint

data_path = "dataset/dataset.yaml"

results = model.train(
    data=data_path,
    epochs=100,
    imgsz=640,
    patience=15,
    device="cuda",
    seed=42,
    save=True,
    name="model_finetuned"
)

Installation 

pip install ultralytics

Example dataset.yaml 

path: Errors_Additive_Manufacturing_Nozzle_Cam
train: images/train
val: images/val
test: images/test

names:
  0: error_type_1
  1: error_type_2
  2: error_type_3
  3: error_type_4

Statistics

Included summary files:

  • summary_splits.csv (images and boxes per split)
  • summary_classes.csv (class distribution)

License

Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)
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Number of rows:
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Total file size:
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