A Comparison of Manual and Automotive FOD Detection Systems at Airport Runways

Jain, Sarika; Prasad, Mani Shankar; Gutpa, Ravina

doi:10.1109/icrito56286.2022.9964557

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, real-time monitoring of airport runways, as well as real-time detection, identification, and localization of FODs, are essential tasks to ensure the safety of air transportation. Currently, the methods widely used for detecting FOD include manual inspection [2], radar detection technology [3][4][5], and optical detection technology [6][7][8]. The manual inspection method may be somewhat effective to some extent, but it lacks efficiency and reliability.…”

Section: Introductionmentioning

confidence: 99%

Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes

Mo,

Wang,

Hong

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract