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For the robot in the tunnel construction environment,due to factors such as dark and strong light,the target detection in the infrared image fails,a method of infrared target detection based on unit statistical curvature feature matching is proposed.The least squares method is used to fit the surface of the target,and the Gaussian curvature and Mean curvature of each pixel in the target are calculated according to the fitted surface.The curvature is used to replace the gradient to construct the image feature descriptor and establish the curvature plane.According to the density of the curvature distribution,the curvature plane is divided into multiple unit areas,and use statistical information for the pixels in each unit to generate a stable unit statistical curvature feature matrix,by calculating the Euclidean distance between the matrices to obtain the similarity of the target,it can find the target to be detected in the infrared image.This algorithm compares and evaluates the detection accuracy of standard image data sets and the actual construction tunnel trestle with other existing algorithms.The result shows that the detection accuracy of this algorithm is the highest,which meets the needs of tunnel robots in engineering to identify trestle bridges.