The prevention of foreign object debris in domestic small and medium-sized airports mainly depends on manual detection. To reduce the cost of detection of foreign object debris in airport flight areas, a method that relies on image detection to identify foreign invasive objects at the airport is proposed. To improve the reliability of the saliency detection, the classical ITTI model is improved. The D-S evidence fusion theory is used to fuse features according to the degree of support, and saliency detection is performed on the processed images. The simulation was performed in 500 samples. The experiment proves that the accuracy of the foreign object recognition in the image is over 92%, which is about 11% higher than the highest color feature detection in the single feature. The unrecognized condition and the detected noise dropped below 10%, which greatly increased the efficiency of foreign object recognition.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Feature Fusion for Airport FOD Detection


    Contributors:
    Chen, Jida (author) / Tang, Xinmin (author) / Ji, Xiaoqi (author)

    Conference:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Published in:

    CICTP 2020 ; 198-208


    Publication date :

    2020-12-09




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Airport Runway Area Detection Based on Multi-Feature Optimization in PolSAR Images

    Han, Ping / Shi, Qingyan / Zou, Can et al. | IEEE | 2018


    Contrastive Multi-Modal Fusion for Enhanced Airport Surface Surveillance

    Chao, Xu / Cai, Kaiquan / Zhao, Peng et al. | IEEE | 2025



    Fatigue Driving Detection Based on Multi Feature Fusion

    Deng, Wanghua / Zhan, Zeyan / Yu, Yi et al. | IEEE | 2019