Yu et al. [8] introduced a multi-image-featured-based method for detecting cracks on concrete using an enhanced salp swarm algorithm applied to a support vector machine to improve the accuracy of crack detection. Additionally, Dong et al. [9] used encoder-decoder networks to perform pixel-level fatigue crack segmentation for steel structures. CrackForest [10] continues to identify cracks using handcrafted features, although they are insufficiently discriminative to distinguish fractures from complicated backgrounds with low level signals. Similarly, Li et al. [11] proposed a multiscale image function to detect cracks on roadway images.
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Recently, Sobol et al. [12] and Park et al. [13] applied a variety of fully convolutional networks (FCNs) in order to detect cracks in pavement. These were trained in an end-to-end approach for pixel-level segmentation and detection. However, when processing some images, the network went into a state in which all pixels were processed as a background; a similar problem was also described in [14], where the method could not detect thin cracks. Oliveilra et al. [15] introduced a method that is based on support vector machines and applied it to pavement images where they were frequently broken into a succession of sub-depicted images during training. However, the method could not precisely segment out fracture curves throughout the entire image because the result is often only a label for the sub-image. Zou et al. [16] and Yang et al. [17] presented DCNNs for crack detection with hierarchical feature learning to identify pavement deterioration.
The abovementioned methods have good results in the automatic detection of road surface cracks. However, these methods have a high rate of false-positive results on images taken in real-world. In other words, images captured by a camera in real-world settings without any post-processing. These images have several defects that increase false-positive results, such as:
Cracks are detected with 0% accuracy by FCN-VGG. There are no cracks observed in samples 1 and 3, as shown in Figure 7. As a result, when the fractures are narrow, the rate of false negatives is considerable. It can be explained due to higher complexity of input images used in this paper as images captured by a camera in real-world settings without any post-processing.
After leaking state secrets to the public, a government contractor named Ezra Kleinfelter (Christian Campbell) is on the run. A mysterious man hires Sherlock and Joan to locate him. Sherlock discovers that his new client works for Ezra's former employers and would most likely kill him if they found him. Sherlock identifies Ezra's connection with "Everyone," a group of cyber-activists, and deduces the identity of the female Everyone member harboring Ezra, but they find Vanessa Hiskie dead in her apartment. They suspect Ezra killed her, probably for resisting his romantic advances, but find a box he took from a bunker where he was previously hiding and use it to trace his new location. Sherlock steals a guard's phone, realizing he is passing messages to Ezra, but Everyone traces it to Sherlock and Joan and wreaks havoc on their digital lives. Meanwhile, one of Joan's friends posts her profile on a dating site. Everyone posts horrible things supposedly written by her, as well as her address. The hackers also disconnect her and Sherlock's cell phones, prompting a man she showed interest in on the dating site to show up at her house to check up on her. Sherlock catches up to Ezra as he tries to leave the country on a plane owned by a very wealthy former software engineer and Everyone member. Ezra threatens to expose 14 American unless they let him go. Sherlock contacts the man who originally hired him on the case, Mr. Honeycutt, and convinces him to release the list of names Ezra has to the authorities so they can get the spies to safe houses. Ezra is extradited and arrested, not for the leaks, but for Hiskie's murder.
A colleague of Gregson's asks Sherlock for help infiltrating the heavily guarded headquarters of a drug-dealing biker gang. Undercover cop Ryan Dunning believes the gang leader stores everything about the gang's operations on a computer in his office. Sherlock plans the heist, but the inside man is killed before they can carry it out. The man's body camera records the moment of his death; he apparently grew impatient and tried to steal the computer by himself, but was discovered and shot. Sherlock spots discrepancies between the recording and the real office and concludes that the video was faked at a different location. The man and his partner wanted to incite a police raid on the gang, but his partner doublecrossed him. When the police search the headquarters, the computer is sent to police officer Lisa Hagen to crack the encryption. However, it turns out that Hagen was Dunning's partner. She is only interested in the gang's offshore bank accounts. She siphons off millions, but is caught.
Sherlock and Joan investigate a mass murder when a chef and several patrons are poisoned after dining on foie gras containing snake venom. The diners were all members of the IAO group, the internet's first line of defense. The sole survivor, a vegetarian, assures the detectives that there are many more defenses, as well as replacements for his deceased colleagues. A video is released, claiming credit for the poisoning and threatening to target the New York Stock Exchange (NYSE). In response, the NYSE shuts down operations for the day so its security can be checked by a team of outside experts. Sherlock realizes the whole thing is simply a cover for the theft of three paintings worth around $60 million displayed in the NYSE building. All the security measures in place were deactivated for the audit. Sherlock has an art forger create copies of the stolen works, and their "recovery" is reported in the media. From darknet chatter claiming the recovery was faked, he is able to identify one of the robbers - one of the security experts who checked the NYSE system - but he turns up dead, bitten by a coastal taipan. Sherlock then identifies his murderous partner, a cook who worked for the chef, by the distinctive bite marks. The cook took the precaution of injecting himself with small doses of the venom to build up an immunity, so he could "milk" the taipan; this proved a wise move, as he was bitten, though it later proved to be his undoing as well.
Being familiar with older versions of Access, I set out to test drive without having to crack open a manual, and simply went about some regular field work. There was not a real learning curve for the software, just some getting used to for the tablet. I looked for new features, noting several that folks had been asking for. The familiar menu structures are all there and I was happy to see a few new ones. There is now a snap-to menu, being able to snap to ends, centers and intersections of imported geometries, like DXF files of plans, XML, and IFC models (BIM). This minimizes the need to calc, or import points created in advance. With the Sync manager it was easy to get thing in and out of Trimble Business Center (TBC). 2ff7e9595c
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