| Citation: | ZHANG J B，REN J，WANG M Q. All-weather airport runway foreign object debris detection based on mixed attention[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（9）：3222-3232 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0500
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The foreign object debris (FOD) detection of airport runway plays an important role in the safe take-off and landing of aircraft. However, the existing detection algorithms in different light and weather runway environments have the phenomenon of missed detection and false detection. Therefore, a YOLOv5 FOD detection algorithm suitable for all-weather airport runway environments is proposed. Firstly, aiming at the problem of feature loss in the pooling process of the original network, a cross stage partial spatial pyramid pooling module is designed, which can adaptively extract deep feature semantic information and enhance the multiscale representation ability of the network. Secondly, the mixed attention module is introduced in the feature fusion part, and the channel and spatial feature weights are redistributed to strengthen the feature differences between FOD and unrelated background elements. Then, a multiscale positioning loss function is designed to improve the detection ability of small targets by adding similarity measures, aiming at the phenomenon that small target FOD are difficult to identify and locate, which leads to missed detection. Finally, the optimized training strategy is used to train the MS-FOD dataset. The experimental results show that the improved algorithm achieves an average accuracy of 95.83%and a recall rate of 94.31%, which is 3.68 and 15.69 percentage points higher than the original YOLOv5, respectively. At the same time, the detection speed FPS is 68 frames per second, which meets the needs of real-time FOD detection. The effectiveness of the proposed algorithm for airport runway FOD detection is effectively verified.
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