Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene.

Railway intrusion detection is important for the safety of the railway operation. However, how to detect intruding events in the scene of unmanned aerial vehicle (UAV) surveillance is a challenging task, especially when there are multiple railway regions or unknown types of intruding objects. To tackle the above problems, a railway intrusion detection method is proposed for the scene of UAV surveillance. It can detect intruding events accurately without pre-setting the intruding object type by detecting the normal railway region and the abnormal railway region with intruding objects rather than recognizing the intruding objects directly. The proposed network consists of a novel Fused-convolutional long short-term memory (Fused-ConvLSTM) module for efficiently propagating spatial and temporal information; several attention modules for adaptively enhancing the feature information, such as the improved feature fusion layer; several lightweight strategies for reducing the computation complexity, such as lightweight feature extraction and width multiplier. Experiments are performed on the real aerial videos obtained by a micro UAV. The experimental results demonstrate that the proposed method can accurately detect railway intrusion events in aerial video, and effectively deal with the case of multiple railway regions and unknown intruding object types. The experimental results also demonstrate that the proposed network can achieve better performance than other object detection networks. • A novel deep learning-based railway intrusion detection method for UAV surveillance scenes is proposed without pre-setting the intruding object type. • A novel convolutional LSTM module called Fused-ConvLSTM is proposed. • An improved feature fusion layer is proposed. • An improved railway clearance intrusion detection network is proposed. • The experiments results based on real aerial videos demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]