LAUSR

Abstract A method of multi-block Single Shot MultiBox Detector (SSD) based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance. To address the limitation of small object detection, a multi-block SSD mechanism, which consists of three steps, is designed. First, the original input images are segmented into several overlapped patches. Second, each patch is separately fed into an SSD to detect the objects. Third, the patches are merged together through two stages. In the first stage, the truncated object of the sub-layer detection result is spliced. In the second stage, a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers. The boxes that are not detected in the main-layer are retained. In addition, no sufficient labeled training samples of railway circumstance are available, thereby hindering the deployment of SSD. A two-stage training strategy leveraging to transfer learning is adopted to solve this issue. The deep learning model is preliminarily trained using labeled data of numerous auxiliaries, and then it is refined using only a few samples of railway scene. A railway spot in China, which is easily damaged by landslides, is investigated as a case study. Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6% and obtains an improvement of up to 9.2% compared with the traditional SSD.