Sensors, Vol. 20, Pages 6070: Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method

Sensors, Vol. 20, Pages 6070: Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method Sensors doi: 10.3390/s20216070 Authors: Matheus Gomes Jonathan Silva Diogo Gonçalves Pedro Zamboni Jader Perez Edson Batista Ana Ramos Lucas Osco Edson Matsubara Jonathan Li José Marcato Junior Wesley Gonçalves Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications. Here, we compared ATSS with Faster Region-based Convolutional Neural Networks (Faster R-CNN) and Focal Loss for Dense Object Detection (RetinaNet ), currently used in remote sensing applications, to assess the performance of the proposed methodology. We used 99,473 patches of 256 × 256 pixels with ground sample distance (GSD) of 10 cm. The patches were divided into training, validation and test datasets in approximate proportions of 60%, 20% and 20%, respectively. As the utility pole labels are point coordinates and the object detection methods require a bounding box, we...
Source: Sensors - Category: Biotechnology Authors: Tags: Letter Source Type: research