Company logos tend to appear rather small in images which poses a challenge for detection.



Social media are an important source of information for market research companies. People uploading images from their daily lifes allow market research companies to gain insights into their consumption patterns. Of particular interest are indicators such as brand popularity or public brand perception. The detection of company logos is an important building block in these analyses which is a classic object detection task.


However, company logos are usually not the intended object when taking a picture. Instead they tend to get caught in the image by accident. As a result, company logos tend to be very small and suffer from low resolution or extreme viewing angles. Reliably detecting such objects is a challenging task -- even for modern deep learning-based pipelines.


We work on improving the reliability of the detection for these hard-to-detect logo instances. At the same time we need to keep the computational overhead low to allow the efficient analysis of large image datasets.


We also maintain the  FlickLogos-47 dataset which we use as a benchmark for our algorithms. For more information please contact  Christian Eggert.




  • Christian Eggert, Stephan Brehm, Anton Winschel, Dan Zecha, Rainer Lienhart. 
    A Closer Look: Small Object Detection in Faster R-CNN.
    IEEE International Conference on Multimedia and Expo 2017 (ICME 2017). Hong Kong, China, July 2017. [ PDF ]

  • Christian Eggert, Stephan Brehm, Dan Zecha, Rainer Lienhart.
    Improving Small Object Proposals for Company Logo Detection.
    ACM International Conference on Multimedia Retrieval 2017 (ICMR 2017). Bucharest, Romania, June 2017. [ arXiv] [ PDF]

  • Christian Eggert, Anton Winschel, Dan Zecha, Rainer Lienhart.
    Saliency-guided Selective Magnification for Company Logo Detection.
    International Conference on Pattern Recognition 2016 (ICPR 2016), Cancun, December 2016. [ PDF ]