@inproceedings{10769185,

title = {Exploitation of Open Source Datasets and Deep Learning Models for the Detection of Objects in Urban Areas},

author = {Elpida Gkouvra and Thodoris Betsas and Maria Pateraki},

doi = {10.1109/ICIPCW64161.2024.10769185},

year  = {2024},

date = {2024-10-01},

booktitle = {2024 IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW)},

pages = {4103-4108},

abstract = {In this work we utilize different open-source datasets and deep learning models for detecting objects from image data captured by a mobile mapping system integrating the multi-camera Ladybug 5+1 in an urban area. In our experiments we exploit sets of pre-trained models and models trained via transfer learning techniques with available open source datasets for object detection, semantic-, instance-, and panoptic segmentation. Tests with the trained models are performed with image data from the Ladybug 5+ camera.},

keywords = {Cameras, Conferences, Context modeling, Data models, deep learning, deep learning models, Image segmentation, mobile mapping, Object detection, open-source datasets, Training, Transfer learning, Urban areas},

pubstate = {published},

tppubtype = {inproceedings}

}