Master Student Electronics Engineering, Computer Science or similar (f/m/x)

Since sidescan sonar image data is scarce, transfer-learning of deep learning models is typically applied. The models are first pre-trained on a large dataset, e.g. ImageNet for classification or MS COCO for detection, and afterwards fine-tuned on the sonar data. Those pre-training datasets consist of natural RGB images. Sonar images, however, are grayscaled intensity images. Thus, learned features which depend on color information are useless. Using a more suited pre-training dataset could improve the classification and detection performance of deep learning models on sidescan sonar images.

In general, recent work has shown that the pre-training dataset has a strong influence when fine-tuning deep learning models, especially when the domain gap (the difference between data in the pre-training and fine-tuning dataset) is large. This is the case for sonar images and standard pre-training dataset like ImageNet or MS COCO. A survey on alternative and more suited pre-training datasets should be carried out in this master thesis. Both computer vision tasks classification and detection should be considered.

Your tasks:

  • literature review on transfer-learning and the influence of the pre-training
  • search for alternative datasets with smaller domain gap (e.g. from medical or radar applications)
  • analyze classification and detection performance for selected datasets
  • compare different deep learning architectures
  • studying electronics engineering, computer science or equivalent
  • knowledge in the fields of deep learning and computer vision
  • knowledge in underwater acoustics and sonar signal processing beneficial
  • programming skills in Python required

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x).Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

DLR - Helmholtz / Deutsches Zentrum für Luft- und Raumfahrt

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Deadline: 2023-05-10
Location: Germany, Bremerhaven
Categories: Computer Engineering, Computer Sciences, Data Science, Electrical Engineering, Electronics, Process Engineering, Programming, Student Assistants,


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