A review on foreign object debris/damage (FOD) and its effects on aviation industry

Chauhan, Tejasv; Goyal, Chirag; Kumari, Deepika; Thakur, Amritap Kumar

doi:10.1016/j.matpr.2020.07.457

Cited by 12 publications

(2 citation statements)

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“…Then, the sigmoid function is used for mapping to obtain the channel attention map 𝐶 ∈ 𝑅 × × , 𝐶 and 𝐹 is input to obtain the output feature map 𝐹 ′ ∈ 𝑅 × × through replication. The calculation of the channel attention module is shown in Equation (1).…”

Section: Attention Modulementioning

confidence: 99%

“…The runway is an essential infrastructure for aircraft takeoff and landing, affecting the airport's operational safety and support capacity. Foreign Object Debris (FOD) [1] poses a direct threat to aircraft safety and is a major safety hazard in air transportation. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Mo,

Wang,

Hong

et al. 2024

Applied Sciences

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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.

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Section: Attention Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%