Deep learning algorithms for building opening detection from UAV images
Programming skill is also mandatory (preferably Python), experience with deep learning frameworks is highly preferred (PyTorch, Keras and Tensorflow)
Object detection is a fundamental topic in computer vision. Recently, deep learning has become an effective method and achieved great success in object detection. In this topic, we will focus on earthquake scenes and try to detect the openings of damaged buildings that are visible from UAV images. An opening can be a window, a door, or an internal structure exposed by the collapse of a wall. The UAV can enter the building through the detected opening and explore the interior of the building to search and rescue trapped people. The aim of this MSc topic is to design and implement an efficient end-to-end trainable deep network for opening detection using UAV images as input.
The student will initially revise the existing literature on single stage and two-stage detectors. The proposed network will predict the bounding boxes of openings in UAV images. Some datasets of understanding façade such as CMP-base can be used to train the network. Besides, we will also need to label some openings from the available earthquake images for training.