# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
###### Abstract  
The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and there is also a potential risk of secondary foreign body intrusion. A deep-learning-based intelligent detection method is proposed to solve the problem of low accuracy and low efficiency of small-scale FOD detection. Firstly, a dual light camera system is utilized for the collection of FOD data. It generates a dual light FOD dataset containing both infrared and visible light images. Subsequently, a multi-attention mechanism and a bidirectional feature pyramid are integrated into the baseline network YOLOv5. This integration prioritizes the extraction of foreign object features and boosts the network's ability to distinguish FOD from complex backgrounds. Additionally, it enhances the fusion of higher-level features to improve the representation of multi-scale objects. To ensure fast and accurate localization and recognition of targets, the Complete-IoU (CIoU) loss function is used to optimize the bounding boxes' positions. The experimental results indicate that the proposed model achieves a detection speed of 36.3 frame/s, satisfying real-time detection requirements. The model also attains an average accuracy of 91.1%, which is 7.4% higher than the baseline network. Consequently, this paper verifies the effectiveness and practical utility of our algorithm for the detection of small-scale FOD targets. foreign object debris; small target detection; dual light; multi-attention; deep learning +
Footnote †: journal: Medicalextraction, and morphology to process and extract the feature information of targets, achieving recognition and classification of targets. However, traditional object detection methods require much time in feature design, and manually designed features have low robustness to target diversity issues. Moreover, traditional image processing techniques are susceptible to variability in imaging conditions such as lighting, occlusion, and defects. As a result, significant variations in the output of these techniques can be observed. This adversely impacts the precision and accuracy achievable in target recognition applications. FOD detection is a challenging task of finding small targets in complex backgrounds [9]. Airport road surfaces are complex and varied, with oil stains, pavement textures, and objects obscuring them. Many researchers from different countries have proposed various detection algorithms based on optical imaging technology. With the advancement of computer hardware, deep learning algorithms, particularly convolutional neural networks (CNN), have experienced significant progress. The detection accuracy of traditional methods is much lower than that of deep learning methods, so most of the current research in target detection is focused on convolutional neural networks. It is also necessary to study intelligent FOD detection algorithms based on images, which can overcome the limitations of manual inspection, radar detection technology, and traditional optical detection technology. [PERSON] et al. [10] proposed the classic two-stage object detection algorithm R-CNN (Region Convolutional Neural Network); [PERSON] et al. [11] proposed the one-stage object detection algorithm You Only Look Once (YOLO); and [PERSON] et al. [12] proposed a single shot multibox detector (SSD) combining the anchor mechanism of Faster R-CNN [13] and YOLO's regression idea. The aforementioned methodology utilizes visible light band imagery of the target as the input data source. Based on extracted visual features, this enables global detection and recognition of the image. Visible light images have rich color and texture features, so most deep learning detection algorithms can achieve good detection accuracy for medium to large targets with sparse distribution in visible light images.