Abstract:At present, a number of computer vision-based crack detection techniques have been developed to efficiently inspect and manage a large number of structures. However, these techniques have not replaced visual inspection, as they have been developed under near-ideal conditions and not in an on-site environment. This article proposes an automated detection technique for crack morphology on concrete surface under an on-site environment based on convolutional neural networks (CNNs). A well-known CNN, AlexNet is trained for crack detection with images scraped from the Internet. The training set is divided into five classes involving cracks, intact surfaces, two types of similar patterns of cracks, and plants. A comparative study evaluates the successfulness of the detailed surface categorization. A probability map is developed using a softmax layer value to add robustness to sliding window detection and a parametric study was carried out to determine its threshold. The applicability of the proposed method is evaluated on images taken from the field and real-time video frames taken using an unmanned aerial vehicle. The evaluation results confirm the high adoptability of the proposed method for crack inspection in an on-site environment.Keywords: crack; deep learning; convolutional neural networks; AlexNet; unmanned aerial vehicle
visual similarity duplicate image finder 4.2 0.1 crack
DOWNLOAD: https://jinyurl.com/2vC2KM
Security skins[171][172] are a related technique that involves overlaying a user-selected image onto the login form as a visual cue that the form is legitimate. Unlike the website-based image schemes, however, the image itself is shared only between the user and the browser, and not between the user and the website. The scheme also relies on a mutual authentication protocol, which makes it less vulnerable to attacks that affect user-only authentication schemes.
Note that these sites search databases and/or use rainbow tables to find a suitable string that produces the hash in question but one can't definitively guarantee what string originally produced the hash. This is an important distinction. Suppose that you want to crack someone's password, where the hash of the password is stored on the server. Indeed, all you then need is a string that produces the correct hash and you're in! However, you cannot prove that you have discovered the user's password, only a "duplicate key." 2ff7e9595c
Comments