# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 1 Introduction
The success of the Transformer architecture in natural language processing inspired its application in computer vision [31]. [PERSON] et al. [32] proposed a complete end-to-end detection model called DETR (Detection Transformer) in 2020. Using a Transformer-based approach effectively eliminates the need for many hand-designed components. In [33], a multi-scale deformable attention mechanism was introduced to replace the multi-head self-attention mechanism in the Transformer to focus the model more on the local information of the reference points. This design alleviates the problem of high computational resource demand in DETR and enables the backbone network to output more levels of feature graph information, allowing the Transformer architecture to effectively detect small targets as well. To address the issue of slow convergence in DETR, [PERSON] et al. [34] investigated the operational mechanisms of the attention module and query within the decoder. The authors adopted an anchor-free approach to incorporate position prior information into the positional query in the decoder. By utilizing four-dimensional positional information, the model is able to focus more effectively on salient regions. This modification significantly enhances both the training efficiency and accuracy of the model. The aforementioned advanced models are all predicated on refinements devised to mitigate the limitations of prior models. However, a dearth of targeted processing methodologies for detecting small FOD targets in practical deployment scenarios persists, resulting in the application of Transformer-based models within the domain of airport FOD detection remaining constrained. The scarcity and uneven distribution of target features present significant challenges for feature extraction. Furthermore, the extraction of small target features is considerably more difficult than that of large targets, thereby restricting the overall effectiveness of existing Transformer-based models. In the Transformer architecture, connecting multi-scale feature maps through dimensionality reduction causes exponential growth in sequence length. This results in sparsely distributed attention weights, making it difficult to focus on key regions [35, 36]. In deep learning networks, spatial information in images is progressively transformed into semantic information after multiple rounds of downsampling of feature maps. The Feature Pyramid Network (FPN) structure effectively integrates shallow spatial information with deep semantic information, emerging as a key approach to address the challenge of small target detection. The FPN achieves feature fusion and spatial alignment via sampling and interpolation. However, this enhancement of target features increases the overall computational overhead. Therefore, research into efficient algorithms for small target detection to improve detection accuracy remains the foremost priority in the field of target detection. This paper introduces a novel approach to overcome these limitations in FOD detection. Different from prior works [32, 33, 34], which focus on general object detection or incremental improvements in Transformer efficiency, the method of this paper leverages optical imaging sensors to collect real FOD images (Figure 1). Combining the characteristics of the target with the limitations of existing methods, this paper introduces novel modules and algorithms derived from the state-of-the-art Dab-Deformable framework. A key innovation is the Lightweight Deep-Shallow Feature Fusion (LDSFF) algorithm. Inspired by axial attention [37, 38], it decomposes feature maps along their axes to compute attention weights per axis while preserving spatial integrity. After generating multi-scale hotspot maps and executing cross-scale positional focus mapping, LDSFF achieves an effective fusion of high-resolution shallow features and deep semantic information, which contrasts with the static integration paradigm of the FPN.