# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 2 Related Works  
Typical airport surface monitoring systems mainly monitor pedestrians, staff, and vehicles on the airport surface, but it must be noted that people are much smaller in size than cars; therefore, small-target surveillance on the airport surface should focus on monitoring people. Owing to the large area of the apron, it takes a significant amount of time for staff to monitor targets on the apron, and sometimes, owing to bad weather, personnel or obstacles on the airport surface may get omitted. If not detected in time, such omissions may lead to serious safety hazards. Additionally, the limitations of ADS-B and SMR make it particularly important to develop a small-target surveillance system. Based on these criteria, vision-based surveillance systems have addressed the issues.  
In recent years, deep convolutional neural networks have been widely used in various vision-based surveillance methods, especially for the detection of small targets. Furthermore, the detection of small targets is widely used in various fields, such as infrared small-target detection [13], defect detection for industry 4.0 [14], and pest detection in smart farming [15]. Such detection methods are divided into two categories according to the processing of candidate frames [16]: (1) One-stage target detection methods based on regression, which take the entire image as an input to increase the receptive field of the targets on the image and regress the position and category information of the targets at different positions on the image. The most representative methods of this category are the YOLO [17; 18] series, SSD [19], and so on. (2) In the two-stage target detection method, the target candidate frame that may exist on the image is first extracted, the candidate frame of each region is classified, and then position regression is performed. Such methods mainly include the R-CNN [20], Fast R-CNN [21], Faster R-CNN [22], and Mask R-CNN [23; 24; 25]. The first group of methods (one-stage methods) have fast detection speeds and a high adaptability to large targets; however, small targets are easily missed. The second category of methods (two-stage methods) exhibit a relatively high accuracy in small-target detection and are therefore highly suitable for this task. Thus, we chose the Mask R-CNN as our baseline.  
However, numerous challenges are encountered in the detection of small targets when using deep learning. First, target detection based on deep learning uses a convolutional neural network (CNN) as a feature extraction tool. When attempting to increase the receptive field in the CNN, the feature map shrinks, and the stride length may be larger than the size of the small targets; this makes it difficult for the small target to be passed on to the deep network during network convolution [26]. Second, in commonly used datasets, such as Microsoft Common Objects in Context (MS COCO), the sample size of small targets accounts for a small proportion of the total targets, and the size difference between large and small targets is relatively significant. This leads to certain difficulties during target-aware network adaptation. Third, owing to the complexity of the airport surface, it is highly difficult for staff to monitor the targets of the apron. Target detection is the basic module of vision-based surveillance and is directly related to the overall performance of the entire surveillance system. However, because the apron targets are too small, they have few pixels, and their shapes and their outlines are unclear. The small targets appear similar to the surrounding background.  
This study improves on the Mask Scoring R-CNN algorithm and proposes a method of small-target detection based on an attention mechanism, as shown in Figure 1. The proposed method can not only obtain the location and category of the small targets, but also finely segment them to obtain the corresponding geometric properties (including the length, width, area, contour, centre, etc., of the targets).