# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 1 Introduction  
If an aircraft encounters safety problems during a flight, the consequences are severe [1]. Ensuring airport safety, in addition to flight safety, is particularly important. When taxing and parking an aircraft, it is necessary to frequently check whether there are other personnel on the airport surface. This is complicated by the fact that the appearance of staff and pedestrians on the airport surface is random and irregular. Therefore, real-time monitoring of staff and pedestrians is challenging [2]. Furthermore, staff and pedestrians can be regarded as small targets relative to the aircraft [3]. Owing to problems such as light reflection and long distances, small targets are difficult to detect [4]. It is necessary to ensure that there are no pedestrians or other obstacles on the apron and flight region during the taxiing and parking of an aircraft [5].  
In response to the first problem, the randomness of airport personnel being present on the airport surface, the detection of targets on the apron and flight region relies mainly on monitoring and surveillance systems [4]. A vision-based surveillance system can effectively reduce labour costs and work pressure on supervisors. Moreover, a vision-based surveillance system produces 7 \(\times\) 24 h of uninterrupted intelligent patrol, which improves patrol efficiency and alarm accuracy, effectively solving the problem of the random appearance of airport personnel. In addition, vision-based surveillance systems can be used to modify traditional security operations and management modes as well as improve the value of monitoring equipment and the efficiency of decision-making [6]. Finally, they provide off-site and intelligent management methods for airport flight areaoperation specifications, quality control, and safety precautions, and improve precise supervision and scientific safety operation management capabilities [7].  
However, the second problem, or the difficulties associated with small-target detection, arise from the following: (1) the automatic dependent surveillance-broadcast (ADS-B) [8; 9] cannot detect non-cooperating targets, such as pedestrians and staff, and (2) the low resolution of the surface movement radar (SMR) [10; 11] leads to the inability to effectively detect small targets. Although vision-based surveillance systems can monitor all targets on the entire apron in real-time and can even detect some small targets, numerous problems are still encountered in the detection of small targets, such as during the fine segmentation and location of such targets and when learning their non-salient features [12]; further, bad weather can result in the omission of personnel or obstacles on the apron. In summary, the above difficulties limit the detection accuracy of small targets in vision-based surveillance systems. This paper proposes a method for small-target detection on the apron to address the aforementioned problem. The contributions of this study can be summarised as follows:  
1. A small-target detection model that can be applied to apron monitoring systems is proposed to monitor small targets (staff and pedestrians) on the airport surface.
2. To enhance small-target detection accuracy, an improved small-target detection method is proposed to better extract and fuse features.
3. A new, standard apron small-target dataset, mainly composed of airport apron pedestrians and staff, is established.
4. The effectiveness and feasibility of the proposed method are verified on a real apron, and small targets are effectively detected, proving that the proposed method can be applied to monitor a real apron.  
The remainder of this paper is organised as follows: Section 2 presents a brief review of recent methods related to small-target detection. The proposed method is described in detail in Section 3. Section 4 presents the experimental results and analysis, including a comparison of the results with those of the current small-target detection algorithm. Finally, in Section 5, we present our conclusions.