# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 4 Results
### Dataset and Ablation Study  
The hybrid dataset comprises 1236 high-resolution airport runway images, all with an image resolution of 2560 \(\times\) 1440. Though limited to two classes, it contains meticulously annotated instances to evaluate algorithm feasibility across categories. As presented in Figure 9, the upper segment of the figure exhibits the existing publicly accessible FOD dataset. This segment is indicative, as the majority of images within the FOD-A collection closely parallel those in Figure 9, featuring FOD objects situated on pristine ground surfaces lacking an authentic airport background. In contrast, the dataset acquired in this paper, as depicted in the lower segment of Figure 9, portrays an airport runway environment, suggesting that the dataset employed for training in this study incorporates complex background context. Within the FOD classification of the dataset in this manuscript, screws demonstrate an aspect ratio of 2:1 with an average dimension of approximately 100 \(\times\) 50 pixels. Similarly, insects exhibit an aspect ratio of 3:2 with an average dimension of approximately 30 \(\times\) 20 pixels. Figure 10 delineates the spatial arrangement of targets across the training and validation datasets. Dab-Deformable, a Transformer-based architecture for small object detection, efficiently leverages attention mechanisms and sequence modeling to capture contextual and positional priors. Its flexibility allows the seamless integration of our proposed improvements. Ablation experiments were conducted on a custom dataset, using Dab-Deformable as the baseline, to verify the performance improvements of the proposed algorithm and module. Figure 10: This set of heat maps shows the spatial distribution of FOD targets in the training set and validation set. Figure 9: The two sets of images from top to bottom are FOD-A dataset images and FOD dataset images collected in this paper. For easy observation, the targets are enlarged in the FOD dataset images collected in this paper. The MDDCA was proposed to enhance the extraction of channel features by the model in conjunction with a multi-directional attention mechanism and deformable convolution. In this study, we conducted a comprehensive comparative analysis to evaluate and compare existing channel attention mechanisms in the backbone network, including Coordinate Attention (CA), Squeeze Excitation (SE), the Spatial Channel Attention Module (CBAM), and Efficient Channel Attention (ECA). This analysis shows that the proposed MDDCA module achieves an optimal performance for FOD detection on airport runways, with a notable improvement in mean average precision (mAP). The best results in the experiments were indicated in bold. As shown in Table 1, in contrast to other modules, the MDDCA effectively improved the model performance. Overall, the channel attention mechanism performed better than all other modules. Compared to the baseline model, the MDDCA module improved mAP\({}_{50-95}\), mAP\({}_{50}\), and recall by 2.4, 2.9, and 1.7 percentage points, respectively. In the Dab-Deformable model, the feedforward network (FFN) in the encoder-decoder architecture relied on simple linear and nonlinear transformations. This static mapping mechanism inherently lacked a dynamic tuning capability. To address this issue, we introduced a feedback mechanism to minimize information loss and prevent overfitting. By integrating the FBFFN, the encoder and decoder were reconstructed in the same way. To validate the performance of the reconstructed encoders and decoders, we trained the model on encoder-only reconstruction, decoder reconstruction, and the combination of both reconstructions in the experiments. The results obtained are shown in Table 2.  
From the experimental results in Table 2, FBFFN-E (encoder-only reconstruction) demonstrates a superior performance across all key metrics.