Multi-network for Joint Detection of Dynamic and Static Objects in a Road Scene Captured by an RGB Camera

Çaldıran B. E., ACARMAN T.

6th International Conference on Inventive Communication and Computational Technologies, ICICCT 2022, Namakkal, Hindistan, 12 - 13 Mayıs 2022, cilt.383, ss.837-851 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 383
  • Doi Numarası: 10.1007/978-981-19-4960-9_63
  • Basıldığı Şehir: Namakkal
  • Basıldığı Ülke: Hindistan
  • Sayfa Sayıları: ss.837-851
  • Anahtar Kelimeler: Deep learning, Drivable area segmentation, Dynamic and static traffic object localization, Lane line detection, Multi-task learning, Pedestrian localization, Traffic light classification
  • Galatasaray Üniversitesi Adresli: Evet

Özet

© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.This study presents a unified network to localize dynamic, static traffic objects and pedestrians, classify traffic light colors, detect drivable area and lane line simultaneously. In the network architecture, traffic object branch is created to classify dynamic objects such as cars, trucks, buses, motorcycle, and bicycle. Static objects are categorized by traffic sign and traffic light objects. Pedestrians are also localized as a separate traffic object group. Traffic light is classified correctly when it is visible. The network design has a unified architecture, one shared encoder for feature extraction and three decoders for three tasks. For benchmarking purposes, the BDD100K dataset is used. The presented model is ranked in the second place for drivable area segmentation, lane line detection, and inference speed while benchmarking with publicly available multi-networks. In comparison with respect to state-of-the art segmentation models re-trained with BDD100K dataset, the task of dynamic object localization’s MIoU metric is reached to the level of 73.54%, which is 40% higher than the results of re-trained segmentation methods.