A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Data-driven analytics applied on UAV imagery using deep learning
This deliverable presents the overall development status of the deep learning analytics applied on UAV imagery (both visual and thermal) on M18 of the project’s lifetime. Within the duration of T3.3, several DL pipelines were designed, implemented, and tested on the task of object detection with humans as the main object of interest. Different variants of the pipelines were investigated including various powerful State-of-the-Art object detection network including Yolov4, scaled-Yolov4, Yolov5, Detectron2 and FasterRCNN. In addition, a novel hybrid inference mechanism was proposed, developed, and tested to cope with the identified challenges especially with respect to the effect of the UAV flight altitude. The proposed inference mechanism combines the output of altitude-dependent local deep learning increasing the generalisation capabilities of the OD system. An extensive experiment set-up was designed to identify the best performing deep learning networks and demonstrate the detection performance of the proposed object detection pipeline utilizing both spectral and thermal information.
Data-driven analytics applied on UAV imagery using deep learning
This deliverable presents the overall development status of the deep learning analytics applied on UAV imagery (both visual and thermal) on M18 of the project’s lifetime. Within the duration of T3.3, several DL pipelines were designed, implemented, and tested on the task of object detection with humans as the main object of interest. Different variants of the pipelines were investigated including various powerful State-of-the-Art object detection network including Yolov4, scaled-Yolov4, Yolov5, Detectron2 and FasterRCNN. In addition, a novel hybrid inference mechanism was proposed, developed, and tested to cope with the identified challenges especially with respect to the effect of the UAV flight altitude. The proposed inference mechanism combines the output of altitude-dependent local deep learning increasing the generalisation capabilities of the OD system. An extensive experiment set-up was designed to identify the best performing deep learning networks and demonstrate the detection performance of the proposed object detection pipeline utilizing both spectral and thermal information.
Data-driven analytics applied on UAV imagery using deep learning
Christos Angelidis (author) / Iosif Sklavidis (author) / Athanasios Siouras (author) / Konstantinos Stergiou (author) / Serafeim Moustakidis (author) / Christodoulos Santorinaios (author)
2021-12-31
oai:zenodo.org:5833868
Paper
Electronic Resource
English
DDC:
690