Intrusion Detection of Abnormal Objects for Railway Scenes Using Infrared Images

Intrusion detection of abnormal objects is critical to avoid traffic accidents and ensure the safety of train operations. Computer-vision based approaches using RGB images have been intensively investigated for intrusion detection at daytime. However, the abnormal object detection using infrared images at nighttime remains more challenging because training samples of infrared images are limited to address this issue, we propose a data augmentation strategy motivated by image style transfer using CycleGAN. First, the synthetic images are generated which conditioned on railway scene images at daytime and non-railway scene images at nighttime. Then, a SSD object detection model is trained using the generated synthetic samples. Finally, the trained SSD model is used to detect abnormal objects for infrared images at nighttime. Experimental results demonstrate that the proposed data augmentation strategy and the object detection method for nighttime scene is effective.