The development of autonomous agricultural vehicles depends on accurate visual interpretation of the environment. Semantic pixel-wise segmentation plays an important role in achieving a more detailed description of the scene and understanding of the spatial-relationship between different objects. In this paper, a deep architecture for road scene and indoor scene segmentation, SegNet, is applied in solving the scene recognition problem for the agricultural environment. The network is trained on an agriculture image dataset collected during field operation. The problem focuses on the segmentation of five classes of structures commonly found in the fields: vegetation, grass, ground, crop field and obstacles. We apply frequency balancing to address the challenge of class imbalance and transfer learning technique. The quantitative performance of the network is evaluated using pixel accuracy and intersection over union metrics. Moreover, we present an approach for an infield qualitative validation of the trained network.
|Title of host publication||Proceedings of the 16th International Conference on Control, Automation, Robotics and Vision|
|Publication status||Published - 2021|
|Event||16th International Conference on Control, Automation, Robotics and Vision (ICARCV)|
- Virtual event, Shenzhen, China
Duration: 13 Dec 2020 → 15 Dec 2020
|Conference||16th International Conference on Control, Automation, Robotics and Vision (ICARCV)|
|Period||13/12/2020 → 15/12/2020|