# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 3 Experiment
### Experimental Dataset Creation and Analysis  
FOD detection presents a challenge when dealing with small targets set against complex backgrounds. These small-scale targets, owing to their diminutive physical dimensions or the considerable distance from the imaging source, occupy a relatively minuscule portion of the image, often encompassing only a few dozen pixels or even fewer. In the context of the COCO dataset [32], small targets are defined as those encompassing dimensions less than 32 \(\times\) 32 pixels. Additionally, according to a definition from the International Society for Optical Engineering [33], a small target constitutes any target encompassing less than 80 pixels within a 256 \(\times\) 256-pixel image. In simpler terms, if the size of the target is less than 0.12% of the original image area, it qualifies as a small target. The visual representation of a small target is shown in Figure 7.  
FOD refers to any foreign objects, debris, or substances that have the potential to cause damage to the aircraft. These can include metal components [34], crushed stones, paper products, animals, plants, and more. Among them, metal components are ingested by the engine, and it is very easy to cause accidents. Based on the distribution of foreign objects in actual airport runway scenes and the technical characteristics of deep learning for sample requirements, an Infrared-Visible Foreign Object Debris Dataset (IVFOD) was designed and constructed. This dataset contains four types of foreign objects: screw; nut; key; bottle. The IVFOD collected in this experiment used cement concrete pavement and asphalt pavement on the Chenggong campus of Kunming University of Science and Technology to simulate the airport runway pavement. The dual light camera was installed on the equipment, and a twisted pair cable was utilized to interface the camera to the computer. The designated shooting angle was 30\({}^{\circ}\), set on the roadside and on the road. However, data collection was conducted over multiple sessions spanning several days; thus, there may have been a deviation of \(\pm\)1\({}^{\circ}\)-2\({}^{\circ}\) in the actual shooting angle for each session. Foreign objects at a distance of 5 m (\(\pm\)1-10 cm), 10 m (\(\pm\)1-10 cm), and 15 m (\(\pm\)1-10 cm) from the  
Figure 7: Instances of small target: (**a**) conventional size target (cars); (**b**) small target (screw, key, nut and bottle). equipment were photographed in the morning, afternoon, and evening. The entire data collection system is shown in Figure 8.  
Firstly, screened the target images captured by the dual light camera and finally determined the number of images in the dataset to be 4137 pairs. Then, 7217 instance targets containing four classes were labeled using Labelimg and converted into PASCAL VOC2007 annotation format files. Using the Random library, 4137 pairs of images were randomly divided into a training set and a validation set in an 8:2 ratio for model training and validation, respectively. In addition, there were 100 sets of images to verify the generalization ability of the final model. The original images and annotation images of FOD are shown in Figure 9.  
The size and distribution of targets used in this experiment are shown in Figure 10. The pixel area encompassed by the targets acquired in this experimental investigation uniformly  
Figure 8: Data collection process. Figure 9: IVFOD dataset images and annotations: (**a**) original infrared image; (**b**) original visible light image; (**c**) infrared annotation map; (**d**) visible annotation map. comprises less than 5000 pixels, and the size of the target is less than 0.12% of the original image area, thereby satisfying the aforementioned criteria for small target classification. In addition, the numbers of the four types of targets collected in this experiment were 2093, 1822, 1768, and 1534, respectively, with a relatively balanced distribution.