Dataset Open Access

PCB DSLR DATASET

martin kampel; Christopher Pramerdorfer

The PCB DSLR dataset is meant to facilitate research on computer-vision-based Printed Circuit Board (PCB) analysis, with a focus on recycling-related applications. The dataset contains 748 images of PCBs from a recycling facility, captured under representative conditions using a professional DSLR camera. All images come with accurate PCB segmentation information as well as bounding box information for all Integrated Circuit (IC) chips (9313 samples).

Statistics

Please refer to [1] for detailed statistics.

  • Camera: Nikon D4 with f/2.8 lens and polarization filter
  • Image resolution: 4928×3280 (about 220 ppi)
  • 165 different PCBs, 3 to 5 images per PCB, 748 images in total
  • 2048 unique labeled ICs, 9313 labeled ICs in total
  • 1740 ICs with label information in total

Download

The dataset is split into 8 parts. APIs for convenient use from Matlab, Python2, and C++ are available on github. The dataset is freely available for non-commercial research use. Please cite  [1] when using the dataset.

References

[1] C. Pramerdorfer and M. Kampel, “A dataset for computer-vision-based PCB analysis,” 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, 2015, pp. 378-381, doi: 10.1109/MVA.2015.7153209. 2015.

Files (6.2 GB)
Name Size
cvl_pcb_dslr_1.zip
md5:480cbb572441f2b6fb013db965d3d4a1
708.9 MB Download
cvl_pcb_dslr_2.zip
md5:8df69ce61a65b92aee058197453b594d
676.9 MB Download
cvl_pcb_dslr_3.zip
md5:b1288448f60e4e06d14ba5a14ccafb96
630.3 MB Download
cvl_pcb_dslr_4.zip
md5:fc02ff0ff932c6774dd0136b4330f4f8
540.5 MB Download
cvl_pcb_dslr_5.zip
md5:49f24f8b4d622cd601cb058cfe49e78c
909.3 MB Download
cvl_pcb_dslr_6.zip
md5:5c7c3a63d487069419d95040c998da71
806.1 MB Download
cvl_pcb_dslr_7.zip
md5:698245b24a835ca10da07b4665e90e84
931.4 MB Download
cvl_pcb_dslr_8.zip
md5:e02e2e5cf2b088d5d0bab4bfa9cd7fad
975.1 MB Download
  • C. Pramerdorfer and M. Kampel, "A dataset for computer-vision-based PCB analysis," 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, 2015, pp. 378-381, doi: 10.1109/MVA.2015.7153209. 2015.

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