The prevention of foreign object debris in domestic small and medium-sized airports mainly depends on manual detection. In order to reduce the cost of detection of foreign object debris in airport flight area, a method that relies on image detection to identify foreign invasive objects at the airport is proposed. In order to improve the reliability of the saliency detection, the classical ITTI model is improved. The Canny operator is used to extract the edge features. The Monte Carlo method and the knowledge of fuzzy mathematics are used to calculate the basic probability of each feature detecting foreign objects. A method is defined to express the support between features. Finally, 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.

CICTP