## 1 Introduction  
As of 2018, the length of high-speed railway lines in China had exceeded 29,000 km. With the increasing of speed, any object intruding railway clearance will pose a major threat to high-speed trains. Effective intrusion detection methods are of great significance to the safety of a high-speed railway operation. Machine vision is an effective non-contact intrusion detection method widely used in railways for the advantage of easy maintenance and intuitive results. Most of these methods detect intruding objects with background subtraction [1]. The disturbance of strong environment light and severe weather will lead to false alarms. To solve this problem, the convolutional neural network (CNN) [2] was used to detect objects intruding railway clearance. CNN has a good rotation and translation invariance, which can effectively reduce the impact of camera shake and light changing. Moreover, CNN has developed rapidly in recent years. From Lenet-5 [3] to Alexnet [4], VGGNe [5], GoogLeNet [6] and ResNet [7], the depth of the CNN network has reached hundreds of layers, and the recognition effect has gradually improved. However, all these networks are classification models. They need to be retrained when the scene changes because of their weak generalization ability. They are ineffective in railway clearance intrusion detection. Therefore, it is of great significance to have a novel CNN network which can detect objects in railway images. At present, object detection methods with CNN are mainly divided into two types. One is based on regional candidate models (such as R-CNN, Fast R-CNN and Faster R-CNN), the other is based on regression models (such as YOLO and SSD). In 2014, [PERSON] [8] proposed the R-CNN model, which extracted candidate regions with a selective search method, extracted candidate region features with CNN, and classified candidate box regions with SVM. But the speed and accuracy were slow. To solve the problem, a Fast R-CNN model [9] was also proposed by [PERSON] in 2015. A box regression algorithm was directly added to the CNN network for training with a multi-task loss function. The mean average precision (mAP) is higher than R-CNN but not fast enough for real-time detection. [PERSON] [10] proposed the Faster-Rcnn model, which used the Region Proposal Network (RPN) to predict the bounding box directly. The recognition time was reduced to 198 ms, since most of the prediction was completed in GPU. In 2016, [PERSON] [11] proposed the YOLO model, which greatly speeded up the detection speed by directly detecting the last feature map in CNN. However, the bounding box location error of YOLO was large. [PERSON] [12] extracted bounding boxes from CNN multi-layer feature maps to improve the speed and accuracy with the SSD model. Among the above algorithms, the SSD model has a higher speed and accuracy. The mAP on the PASCAL VOC 2007 dataset was 74.3%, and the elapsed time was only 36.2 ms in GeForce GTX1060. However, the detection performance of SSD for small objects is unsatisfactory, and the effect directly used in railway clearance intrusion detection is poor. In order to solve this problem, this paper proposes an improved SSD method for railway clearance intrusion detection. The SSD is improved on the basis of the DeepLab network structure [13]. High-level features are deconvolved to low-level and fused with original low-level features to enhance their semantic information and improve the detection accuracy. In order to reduce the model parameters and calculation load within a permitted loss of accuracy, the unimportant weights in the network are pruned with the L1 norm criterion [14]. Our main contributions are as follows:  
* The deconvolution structure is introduced into the SSD network to improve the detection ability of small objects. \(19\times 19\) and \(38\times 38\) feature maps are deconvolved to and fused with \(38\times 38\) and \(75\times 75\) feature maps, respectively.