基于自適應閾值和灰度共生矩陣的路面裂縫檢測方法
首發時間：2025-09-19
汪義娟（2000-），女，碩士研究生，交通基礎設施災害識別，計算機視覺
馬海峰 1馬海峰（1984-），男，安徽淮北人，副教授，交通運輸規劃
摘要：針對傳統路面裂縫檢測方法在復雜場景下噪聲敏感、特征單一及精度不足等問題，提出了一種自適應閾值濾波和灰度共生矩陣的路面裂縫檢測方法。該方法集成高斯、中值及改進雙邊濾波算法，通過自適應權重分配策略動態優化噪聲，結合形態學骨架提取與灰度共生矩陣（GLCM）紋理分析進行綜合表征裂縫形態、紋理及空間分布特征，并采用激光雷達和三維點云實現裂縫長度、寬度等參數自動統計，采用美國馬薩諸塞州公路損傷目標檢測數據集驗證所提方法。實驗結果表明，該方法在噪聲干擾條件下圖像信噪比提升8 dB以上，裂縫檢測精確率達96.78%，誤檢率降至3.8%，單幀處理時間僅1.2秒，效率提升3倍；點云配準誤差小于0.15 mm，曲面擬合度優于95%。與傳統高斯、雙邊、維納、Gabor及導向濾波方法對比，驗證了本文方法在復雜環境下具備良好的魯棒性與識別精度。該研究為多源干擾場景下的路面裂縫檢測提供了有效技術路徑，并可拓展應用于橋梁、隧道等結構病害識別。
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Pavement crack detection method based on adaptive threshold and grey scale covariance matrix
汪義娟（2000-），女，碩士研究生，交通基礎設施災害識別，計算機視覺
MAHaifeng 1馬海峰（1984-），男，安徽淮北人，副教授，交通運輸規劃
Abstract：Aiming at the problems of noise sensitivity, single feature and insufficient accuracy of traditional pavement crack detection methods in complex scenes, an adaptive threshold filtering and grey scale co-production matrix pavement crack detection method is proposed. The method integrates Gaussian, median and improved bilateral filtering algorithms, dynamically optimizes the noise through adaptive weight allocation strategy, combines morphological skeleton extraction and grey scale symbiotic matrix (GLCM) texture analysis to comprehensively characterize the crack morphology, texture and spatial distribution, and uses LiDAR and 3D point cloud to realize automatic statistics of crack length, width and other parameters, and adopts the target of damage detection dataset of the state highway of Massachusetts in the United States to validate the proposed method. The proposed method is validated using the Massachusetts Highway Damage Detection Dataset. The experimental results show that the method improves the signal-to-noise ratio of the image by more than 8 dB under the noise interference condition, the crack detection accuracy reaches 96.78%, the false detection rate is reduced to 3.8%, the processing time of a single frame is only 1.2 seconds, which improves the efficiency by three times; the point cloud alignment error is less than 0.15 mm, and the surface fitting degree is better than 95%. Comparing with the traditional Gaussian, bilateral, Wiener, Gabor and guided filtering methods, the method in this paper is verified to have good robustness and recognition accuracy in complex environments. This study provides an effective technology path for pavement crack detection under multi-source interference scenarios, and can be extended to identify structural diseases in bridges and tunnels.??
Keywords： Pavement cracks Adaptive thresholding Grey scale covariance matrix Morphological skeleton
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基于自適應閾值和灰度共生矩陣的路面裂縫檢測方法
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