pup detection

Transferred from: http://lanbing510.info/2017/08/28/YOLO-SSD.html Prior to the emergence of deep learning, the traditional target detection method is probably divided into regional selection (sliding window), feature extraction (SIFT, hog, etc.), classifier (SVM, adaboost, etc.) three parts, the main problems have two aspects: on the one hand, sliding window selection strategy is not targeted, time complexity, window redundancy On the other hand, the

Objective In the DirectX SDK, the correlation function for collision detection is in xnacollision.h. Now, however, the previously implemented correlation functions have been transferred to the Windows SDK DirectXCollision.h and are in namespace DirectX. This consists mainly of four bounding boxes (bounding Volumes), and is implemented in the form of classes: Boundingsphere class-Surround ball (bounding Box) BoundingBox Class--axis aligned bou

1. The existence and development inevitability of IDS (Intrusion Detection System) (1) complexity of network security itself, and passive defense methods are not powerful. (2) related firewalls: devices with network boundaries can be attacked by themselves, and some attacks are poorly protected. Not all threats come from outside the firewall. (3) Easy intrusion: Intrusion tutorials can be seen everywhere; various tools are at your fingertips; 2. Intru

1. The existence and development inevitability of IDS (Intrusion Detection System) (1) The complexity of network security and passive defense methods are insufficient. (2) related firewalls: devices with network boundaries can be attacked by themselves, and some attacks are poorly protected. Not all threats come from outside the firewall. (3) Easy intrusion: Intrusion tutorials are everywhere; various tools are at your fingertips 2. Intrusion

Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE Conference on Computer vision and pattern recognition. 2014.The full name of R-CNN is REGION-CNN, which can be said to be the first algorithm to successfully apply deep learning to target detection. The fast r-cnn, Faster r-cnn are all based on R-CNN.Most of the traditional targ

Ren, Shaoqing, et al. "Faster r-cnn:towards Real-time object detection with region proposal networks." Advances in neural information processing Systems. 2015.After Rcnn[1],fast Rcnn[2], this article is another masterpiece of the Ross Girshick team, the leader of the target detection community in 2015. The detection speed of simple network target is 17fps, the ac

One, web monitoringWeb Scenarios (Web scene) is used to monitor web programs, can monitor the download speed of Web programs, return code and response time, but also support a set of continuous web actions as a whole to monitor.1, the principle of web monitoringWeb monitoring is the monitoring of the HTTP service, simulating the user to visit the site, to compare specific results, such as status code, return string, and other specific data for comparison and monitoring, so as to determine the av

Image preprocessing Several typical noises in digital images are: The Gaussian noise originates from the noise of the electronic circuit and the sensor noise caused by low illumination or high temperature, and the noise of salt and pepper is similar to the particles of pepper and powder which are randomly distributed on the image, mainly by the image cutting or the error caused by the transform domain; In general, the introduction of the additive random noise, mean filter, median filter, Gaussia

Computer popularization, broadband prevalence, now many friends online time is very long, so the security of the system has become "the most important." Fortunately, many security sites now provide online detection services, can be a good help us to detect the existence of their own computer vulnerabilities and security risks, so that timely and effective solutions to these problems. 　　 first, the Internet Assistant Computer physical examination In

The target detection algorithm of the RCNN series previously studied was to extract the candidate regions, then use the classifier to identify the regions and position the candidate regions. The process of this kind of method is complex, there are some shortcomings such as slow speed and difficulty in training. The YOLO algorithm considers the detection problem as a regression problem, uses a single neural

Sobel Edge Detection algorithmReprint Please specify source: http://blog.csdn.net/tianhai110The Bell operator (Sobel operator) is mainly used for edge detection, and technically, it is a discrete difference operator, which is used to calculate the approximate value of the grayscale of the image luminance function. Using this operator at any point in the image will produce a corresponding grayscale vector or

Win2000 server Security Configuration, a carefully configured Win2000 server can protect against more than 90% intrusion and infiltration, but system security is a continuous process, with the advent of new vulnerabilities and server application changes, the security situation of the system is also changing At the same time, because of the contradictory unity of attack and defense, the magic long and the magic is also in constant conversion, so the system administrator can not guarantee that a s

Settings > Security > No onbody detection option under Smart lock[DESCRIPTION] Settings > Security >smart Lock does not have the on-body detection option. [Solution] A description of this feature can be found on Google's official website: Https://support.google.com/nexus/answer/6093922?p=personal_unlockingrd=1 This feature is available on specific devices only. When you carry your device with you (for e

Hog Characteristics of image feature extraction from target detection Zouxy09@qq.com Http://blog.csdn.net/zouxy09 1. Hog Features: The directional gradient histogram (histogram of oriented Gradient, HOG) is a feature descriptor used for object detection in computer vision and image processing. It is characterized by calculating and statistic the gradient direction histogram of local region of image. Hog f

.1.2.2 Training data (x, y), X for the picture, assuming 32*32*3, Y for the label, need to represent the classification and positioning of the position box, such as y= (PC, BX, by, BH, BW, C1, C2, C3), pc=1 that the picture target for pedestrians, cars, motorcycles, pc=0 means no target , as a background picture. The C1,C2,C3 is used to indicate which category the target is specifically classified. such as y= (1, 0.3, 0.6, 0.3, 0.4, 0, 1, 0) indicate the target for the car; y= (0,?,?,?,?,?,?,?)

Wunda Automatic driving target detection data set: Automatic driving target detection autonomous Driving-car detection Welcome to your Week 3 programming assignment. You'll learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook is described in the YOLO Papers:redmon et al.,

With the increase in hacker technology, wireless LAN (WLANs) is under more and more threats. Session hijacking and DoS attacks caused by misconfiguration of wireless base stations (WAPs) affect the security of Wireless LAN. Wireless networks are not only attacked based on the traditional wired network TCP/IP architecture, but may also be threatened by the security issues of the 802.11 standard issued by the Institute of Electrical and Electronics Engineers (IEEE. To better detect and defend agai

Now with the improvement of hacker technology, the wireless local area network (WLANS) is threatened more and more. The failure to configure a wireless base station (WAPS) causes session hijacking and denial of service attacks (Dos) to be like a plague that affects the security of wireless LANs in general. Wireless networks are vulnerable not only to the traditional wired network TCP/IP architecture but also to the security issues of the Institute of Electrical and Electronics Engineers (IEEE) r

In the previous blog feature point detection learning _1 (SIFT algorithm), the classical SIFT algorithm is introduced briefly, the SIFT algorithm is stable, the detected feature points are also more, the biggest determination is the high computational complexity. There are many scholars to improve it, in which the more famous is the surf algorithm introduced in this paper, the Chinese meaning of surf is fast robust feature. This article is not specifi

= =| | ), this method is ignored directly, so the final decision is to use Method 1.In fact, Method 1 still has a lot of ways to improve, and then refer to the 12 floor method of this post, using indexed array with associative array, improve the efficiency of the retrieval, even the steps of the word-breaker are omitted. The entire implementation code is as follows. Import Org.apache.commons.lang.stringutils;import Org.apache.commons.io.fileutils;import Org.apache.commons.lang.stringutils;impor