Detection and location of objects is a challenging issue in mobile mapping data processing for reducing human operations and enhancing efficiency, considering the vast amount of data and information acquired by mobile mapping systems. This paper describes research results of algorithms based on Hopfield neural networks for utility object detection and location. Specifically, street light poles are modeled in the three-dimensional (3D) scene domain and detected by the network with neurons formed by vector edge features from the model and the mobile mapping images. The established Hopfield neural network is able to detect light poles at specific locations. It can also be used to detect and locate all light poles from a mobile mapping sequence, regardless of their positions. Such automation is particularly important for automatic generation of special layers in a utility GIS, for example, traffic signs, fire hydrants, road centerlines, and others. The developed algorithms and implementation results are described.
|Number of pages||7|
|Journal||Photogrammetric Engineering and Remote Sensing|
|Publication status||Published - 1999 Oct 1|
All Science Journal Classification (ASJC) codes
- Computers in Earth Sciences