Master's Thesis: Detection and Segmentation of Suggestive Clothing

Background/Motivation: Models that can recognise human skin, body parts, or scenes are often used to detect erotic and pornographic material. With the help of appropriate datasets [1], classification and object detection models can be trained. However, there are also images that are obviously erotic or pornographic, but cannot be recognised by conventional methods. This applies, for example, to people in skin-tight latex or leather clothing. Existing approaches in the field of "Human Parsing" can already segment people and their clothing well. Additionally, datasets like Fashionpedia [2] exist, which include segmentation masks and labels for clothing items.



Objective: The aim of this master's thesis is to investigate whether and to what extent clothing items can be used for the recognition of erotic and pornographic imagery. First, it should be researched which existing approaches are suitable for addressing the question. Gaps in existing datasets and models should be described and filled with our own data and models. Based on the developed methods, it should then be evaluated whether (1) reliable detection of erotic clothing is possible and (2) whether erotic and pornographic images can be distinguished from other categories based on the recognised clothing. In this context, different counter classes should be evaluated, such as everyday, sports, or beach images.


Results: As part of this master's thesis, the following results are to be achieved:

• Dataset with annotations for the detection of erotic clothing.


• Implementation of new approaches for the detection and segmentation of erotic clothing items.


• Classification of the detected garments.


• Evaluation of the models, both in terms of object detection/segmentation and classification (pornographic/erotic/normal).

Be part of change

• Building a dataset for object detection or segmentation.


• Use of pre-trained state-of-the-art models like SAM 3 to generate annotations.


• Training models like YOLO, RT-DETR, Mask R-CNN.


• Analysis of existing datasets regarding the clothing present.


• Evaluation of the trained models on suitable datasets.

What you contribute

• Good knowledge in the field of machine learning and training neural networks.


• Good Python skills, preferably some experience with PyTorch.


• Ideally, knowledge in computer vision and object detection/segmentation.


• Motivation to independently delve into new and current research topics.


• Willingness to work with erotic or pornographic material.


• Interest in scientific research. 

What we offer

• Independent work schedule management


• Insights into the intersection of academic research and industrial application.

Related works: 

[1] Phan, D. D. et al., LSPD: A Large-Scale Pornographic Dataset for Detection and Classification — https://inass.org/wp-content/uploads/2021/09/2022022819.pdf
[2] Jia, M. et al., Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset — https://arxiv.org/pdf/2004.12276

We value and promote the diversity of our employees' skills and therefore welcome all applications – regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Our tasks are diverse and adaptable – for applicants with disabilities, we work together to find solutions that best promote their abilities.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.

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