基于改進FasterR-CNN的機場跑道道面裂縫檢測方法

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中圖分類號：TP391.92;U418.6 文獻標志碼：A文章編號：1001-5922（2025）05-0159-04

Abstract：Civil aviation plays a vital rolein China's transportationsystem.With theextension of theservice life of airports，the problem of pavement damage is becoming more and more serious，which poses a major threat to the safetyof aircraft taxing，take-offand landing.Inorder to reduce theriskofaircraft inthe processof take-off and landing，an improved detection method based on Faster R-CNN was proposed.The detection method comprehensivelyused deep learningand objectdetection technologiessuch as GC-ASFF module，CIoU index，improved loss functionand transfer learning to achieve accurate detectionof pavement cracks，so as to evaluate thecurent pavement safety status by using the identified characteristic parameters of pavement cracks.The experimental results showedthat the improved model had high recognition accuracyand excelent comprehensive performance，and can accurately identify and detect runway pavement damage，which has high reliability.

Key Words：crack detection；faster R-CNN；ASFF ；merging ratio；loss function

機場跑道是飛機起降的主要區(qū)域，在荷載和環(huán)境因素的相互作用下，機場道面很容易出現(xiàn)各種病害，裂縫病害則是大多數(shù)問題的早期表現(xiàn)之一，若不及時進行處理，則會造成巨大的安全隱患[1]。（剩余7028字）