# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 1 Introduction  
Foreign Object Debris (FOD) is a critical concern in aviation, as it poses a significant risk to aircraft. FOD typically consists of small objects such as metal parts, concrete fragments, vegetation, and wildlife. According to conservative estimates, annual global direct losses from FOD range from 3 billion to 4 billion [1], with indirect losses from flight delays being substantial. The aircraft damage caused by FOD cannot be ignored. Tire pressure inspections must be performed for each flight mission to ensure the safety of the flight. Additionally, FOD can be sucked into engines, causing failure or damage. This issue shortens the engine maintenance cycle and generates additional maintenance costs. In this context, more investment has been directed in technology research and development in related fields of FOD detection to avoid air accidents [2, 3, 4, 5, 6, 7]. Recently, FOD detection methods have been expanded from manual inspection to radar-based detection systems [8, 9, 10], optical image identification [11], and integrated FOD detection systems that combine radar and optics [12]. Manual inspection was the earliest and most common method for FOD detection. This approach is time consuming and labor intensive, and it can indirectly reduce the flight frequency. With manual inspections,security risks may not be immediately identified or eliminated. Photoelectric recognition based on optical systems can provide high-resolution images and accurately detect targets. However, the performance of a photoelectric system is affected by the light intensity, particularly under low-visibility conditions such as nighttime, rain, snow, and fog, when the visible light is weak. Radar-based detection systems can operate in all weather conditions with a fast response speed for locating targets. However, FOD only exhibits the state of weak scattering points in radar images, which cannot effectively utilize the contour information to accurately identify small targets. To address these issues, high-resolution FOD images are captured by an optical camera after the radar has located the target. The FOD detection problem is essentially a small-target detection problem. In traditional algorithms [13; 14; 15; 16; 17; 18; 19; 20], FOD detection methods use prior knowledge and techniques to design effective feature extractors. These traditional algorithms perform well in specific scenes and for particular targets. However, their generalization ability is limited when the environment or image features of targets change, leading to reduced performance. With the development of deep learning, neural-network-based target detection methods have gradually replaced these traditional methods and achieve better performance and generalization capabilities. Current deep learning algorithms for airport FOD detection can be divided into three main categories: two-stage [21; 22; 23], one-stage [24; 25; 26; 27; 28], and Transformer-based models. Both two-stage and single-stage models are fundamentally based on the design of anchors, which implies that the training and detection in the model depend on complex preprocessing and post-processing steps. The design of anchor boxes in Faster R-CNN must consider the characteristics and scenarios of the target detection task, which commonly must be determined based on the size and aspect ratio of the target in the dataset and subjected to NMS (Non-Maximum Suppression) [29] in post-processing; in YOLO, it must be subjected to mesh delineation and anchor box design in preprocessing and the same NMS operations in post-processing [30]. Both approaches rely on prior knowledge to design anchors, leading to reduced detection accuracy for small FOD targets in complex backgrounds. For a radar-guided, optically confirmed FOD detection system in airports, an end-to-end Transformer-based model meets these needs exceptionally well. This model focuses on critical image regions, enhancing the detection system's robustness and performance.