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

Author:

Mo Yiming1,Wang Lei1,Hong Wenqing2,Chu Congzhen3,Li Peigen1,Xia Haiting1

Affiliation:

1. Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China

2. Kunming Institute of Physics, Kunming 650223, China

3. Yunnan Airport Group Co., Ltd., Kunming 650500, China

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.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

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