Multi-scale underwater image enhancement with optimized homomorphic filter and RGB color correction

Peng, Yang; Wu, Heng; Wang, Tao; Cheng, Lianglun; Zhao, Genping

doi:10.1007/s10043-022-00762-z

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…It improves the effect of crack segmentation and extraction by highlighting the features and details of the intercrack region while weakening the background of uninteresting regions. At present, there are two types of common image enhancement methods: the enhancement methods based on the spatial domain (space domain) [13][14][15], which primarily include point pixel processing and area processing; the enhancement methods based on the frequency domain (frequency domain) [16][17][18], which mainly include low-pass filtering, high-pass filtering, homomorphic filtering, etc. This paper focuses on the image enhancement method based on the spatial domain.…”

Section: Crack Image Datasetmentioning

confidence: 99%

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Li,

Yin,

Zhang

2023

Sensors

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Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

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