CAJ | 학술논문

路面裂缝形态复杂、表观差异较大,难以用明确的特征来表示,而通常的wavelet、Gabor变换及其函数都是预定义的,不能适应路面裂缝图像的特点,为此提出一种新颖的基于生物启发模型（BIM）特征的弹性领域联合最大化处理识别算法,采用弹性邻域,先对相邻四邻域或八邻域进行图像分割,并在每一区域引入Adaboost分类器选择,保留关键信息,去掉无用或负面信息.该算法获得的特征向量全面反映了原图像信息,且计算复杂度低,有利于实时应用.实验结果表明：本文所提出的方法在路面裂缝的总体识别率高达99.13%,且响应时间快,充分显示了本方法的有效性.
로면렬봉형태복잡、표관차이교대,난이용명학적특정래표시,이통상적wavelet、Gabor변환급기함수도시예정의적,불능괄응로면렬봉도상적특점,위차제출일충신영적기우생물계발모형（BIM）특정적탄성영역연합최대화처리식별산법,채용탄성린역,선대상린사린역혹팔린역진행도상분할,병재매일구역인입Adaboost분류기선택,보류관건신식,거도무용혹부면신식.해산법획득적특정향량전면반영료원도상신식,차계산복잡도저,유리우실시응용.실험결과표명：본문소제출적방법재로면렬봉적총체식별솔고체99.13%,차향응시간쾌,충분현시료본방법적유효성.
Due to the complexity of shape and apparent differences of pavement cracks, it is difficult to characterize them with definite features. The wavelet, Gabor transform and its functions are usually predefined and cannot adapt to the characteristics of the pavement crack images. This paper proposes a novel joint maximization recognition algorithm in the resilient area, which is based on the characteristics of biologically inspired model (BIM). The algorithm uses the elastic neighborhood, the first adjacent neighbors domain or eight neighborhood image segmentation. Adaboost classifier is introduced in each region to select and retain key information, get rid of unwanted or negative information. Its eigenvectors can reflect the information in the original image comprehensively and its low computational complexity is helpful in real-time applications. The experimental results show that the overall recognition rate of the proposed method in pavement cracks is up to 99.13%, and its fast response time fully demonstrate the effectiveness of this method.