Potholes are one type of pavement surface distresses whose assessment is essential for developing road network maintenance strategies. Existing methods for automatic pothole detection either rely on expensive and high-maintenance equipment or could not segment the pothole accurately. In this paper, an asphalt pavement pothole detection and segmentation method based on energy field is put forward. The proposed method mainly includes two processes. Firstly, the wavelet energy field of the pavement image is constructed to detect the pothole by morphological processing and geometric criterions. Secondly, the detected pothole is segmented by Markov random field model and the pothole edge is extracted accurately. This methodology has been implemented in a MATLAB prototype, trained, and tested on 120 pavement images. The results show that it can effectively distinguish potholes from cracks, patches, greasy dirt, shadows, and manhole covers and accurately segment the pothole. For pothole detection, the method reaches an overall accuracy of 86.7%, with 83.3% precision and 87.5% recall. For pothole segmentation, the overlap degree between the extracted pothole region and the original pothole region is mostly more than 85%, which accounts for 88.6% of the total detected pavement pothole images.
Based on the research and analysis of video sequence of the Intelligent Transport Systems,this paper especially had a in-depth discussion about the critical step of video detection—shadow removing, analyzing the causes and characteristics of the shadow,describing the current shadow removal algorithms,and proposed a new method of image texture-based vehicle cast shadow elimination approach based on the existing algorithms.Experimental results have proved that this method can remove the vehicle shadow well and still hold very complete target vehicle information and laid the foundation for extracting vehicle target.
Spectral imaging technology research is becoming more extensive in the field of examination of material evidence. Near-Infrared spectral imaging technology is an important part of the full spectrum of imaging technology. This paper finished the experiment contents of the Near-Infrared spectrum imaging method and image acquisition system Near-Infrared spectral imaging technology.The experiment of Near-Infrared spectral imaging method obtains the image set of the Near-Infrared spectrum, and formats a pseudo-color images to show the potential traces successfully by processing the set of spectral images; Near-Infrared spectral imaging technology explores the technology method of obtaining the image set of Near-Infrared spectrometer and image acquisition system, and extensive access to the Near-Infrared spectrum information of latent blood, stamp and smear fingerprints on common objects, and study the characteristics of the Near-Infrared spectrum. Near-Infrared spectroscopic imaging experiments explores a wide variety of Near-Infrared reflectance spectra of the object material curve and its Near-Infrared spectrum of imaging modalities, can not only gives a reference for choosing Near-Infrared wavelength to show the object surface potential traces of substances, but also gives important data for the Near-Infrared spectrum of imaging technology development.
This paper presents a new idea of Epipolar Slope. There are some feature points, e.g. the inflection point, on the epipolar line or curve. The homonymous points can be discovered in a couple images, and Feature Matching can be achived as visible measurement,. The Improved Hough Transform has been discussed in this paper because the standard quadratic curve may be discovered by the Improved Hough Transform as the pre-process of the Feature Matching.
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