A Dark Target Detection Method Based on the Adjacency Effect: A Case Study on Crack Detection

Abstract

Dark target detection is important for engineering applications but the existing methods do not consider the imaging environment of dark targets, such as the adjacency effect. The adjacency effect will affect the quantitative applications of remote sensing, especially for high contrast images and images with ever-increasing resolution. Further, most studies have focused on how to eliminate the adjacency effect and there is almost no research about the application of the adjacency effect. However, the adjacency effect leads to some unique characteristics for the dark target surrounded by a bright background. This paper utilizes these characteristics to assist in the detection of the dark object, and the low-high threshold detection strategy and the adaptive threshold selection method under the assumption of Gaussian distribution are designed. Meanwhile, preliminary case experiments are carried out on the crack detection of concrete slope protection. Finally, the experiment results show that it is feasible to utilize the adjacency effect for dark target detection.

Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures Based on Crack Image Analysis

“…The cracks need to be of sufficient width to appear as dark lines, which is theoretically the width of a pixel. Edge detection methods [25,26,27,28] or machine learning methods [29,30,31,32] are typically employed to identify the locations or widths of cracks. A review of crack detection methods can be found in [33].…”

“…At each level, log (1/ ε ) and log ( N ) can be calculated, as shown in Figure 6c. Further details of calculating the fractal dimension can be found in [26]. Note that, typically, the actual meshes in FAC analyses are more refined, and the number of discretization levels is greater (e.g., 4 levels or higher) than as shown in Figure 6.By applying multi-level mesh refinements (i.e., different sizes of ε ), log ( N ) versus log (1/ ε ) can be plotted on a 2D plot.…”