本文選題：圖像處理 + 航拍目標檢測�。� 參考：《光學(xué)學(xué)報》2017年08期

【摘要】：針對航拍瀝青路面圖像識別的噪聲和干擾問題,提出一種應(yīng)用于航拍圖像的路面裂縫識別算法。根據(jù)路面區(qū)域與路旁景觀區(qū)域灰度級數(shù)分布不同,采用多方向擬合的區(qū)域生長方法聯(lián)合HSV顏色空間閾值進行路面區(qū)域分割,提取包含完整裂縫信息的單通道路面;再通過改進的形態(tài)學(xué)濾波剔除面積較大的干擾區(qū)域,利用結(jié)合顯著性分析的邊緣檢測算法識別路面的裂縫片段,實現(xiàn)復(fù)雜裂縫與路面紋理噪聲的區(qū)分;自動篩選存在裂縫的圖像,針對裂縫可疑區(qū)域,結(jié)合人眼輔助觀察標記并計算其長度。結(jié)果表明,該算法可有效剔除圖像中的噪聲和干擾,較好地識別瀝青路面的裂縫,裂縫寬度的識別精度能達到9.7mm,分類識別準確率大于80.0%,長度測量準確率大于75.0%。
[Abstract]:In order to solve the problem of noise and disturbance in aerial image recognition of asphalt pavement, an algorithm of pavement crack recognition is proposed. According to the different grayscale series distribution between the road surface area and the roadside landscape area, the multi-direction fitting region growth method combined with the HSV color space threshold is used to segment the pavement area, and the single channel pavement containing the complete crack information is extracted. Then the improved morphological filter is used to eliminate the large area of interference and the edge detection algorithm combined with salience analysis is used to identify the crack segment of the road surface to distinguish the complex crack from the road texture noise. The images with cracks are automatically screened, and the length of the cracks is calculated by combining with the human eye observation marks. The results show that the algorithm can effectively eliminate the noise and interference in the image, and better identify the cracks of asphalt pavement. The recognition accuracy of crack width can reach 9.7 mm, the accuracy of classification recognition is more than 80.0, and the accuracy of length measurement is more than 75.0.
【作者單位】： 北京理工大學(xué)光電學(xué)院光電成像技術(shù)與系統(tǒng)教育部重點實驗室;北京理工大學(xué)宇航學(xué)院;
【基金】：國家自然科學(xué)基金(61575023)
【分類號】：TP391.41;U418.6

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