The result implement high performance. Every single line describes one bounding box. The last category, in the paper . Resulted dataset was divided into sub-datasets amount of RGB images. The total amount of images for training and , and images, respectively.
Before validation is and Consequently, coordinates were converted accuracy . To height. Calculations were made by the prohibitory, danger, mandatory and others. Then, the mean following equations: of these calculated APs across all classes produces mAP. AP, in turn, is calculated by considering an area under interpolated Precision axis y and Recall axis x curve [16, 17]. The curve represents performance of the trained model-1 by plotting a zig-zag graph of Precisions values against Recalls values.
Firstly, 11 points are located on Recall curve as following: 0; 0. Then, the average of maximum Precision values is computed for these 11 Recall points. Precision illustrates how accurate predicted bounding boxes are and demonstrates an ability of the model-1 to detect relevant objects.
Recall illustrates all correct where Xmin, Ymin, Xmax and Ymax are original coordinates; predictions of bounding boxes among all relevant ground w and h are real image width and real image height truth bounding boxes and demonstrates an ability of the respectively. Next, Precision and Recall are calculated number of ground truth bounding boxes that is As can be seen from Table 3, the total number of detections at iterations is It means, that there are 3 and 4 bounding boxes with wrong predictions FP for where TP True Positive represents the number of the classes, mandatory and other, respectively.
Nine ground bounding boxes with correct predictions; FP False truth bounding boxes among were not detected FN. If IoU is in the range 0; threshold , Memory. Consequently, mAP for the entire model-1 can be in the range from 6 to 1 respectively. These platforms have Model-1 used Darknet framework to be trained in.
RAM in the range of 4—32 Gb and can process minimum Parameters used for the training are described in Table 2. Before feeding to the network, the for the proposed method after training. Due to a number of categories Images were also collected in the batches with 64 and small amount of images in the dataset for training, items each.
Sixteen was set as a number of subdivisions. As for detection, were processed during one iteration. Weights were updated after each such iteration. To predict bounding boxes, anchors priors were used Table 2. Parameters for the model-1 to be trained with at each scale. The anchors were calculated by k-means clustering for COCO dataset. The last three anchors, scale 1 large object , 90 , , , parameters in Table 2 randomly changed saturation, , exposure and hue during training.
It also shows the highest exposure 1. The images for validation have unique hue 0. Loss and mAP graph during training of model-1 4 categories. In , the authors used among one of the 43 classes. In , the mAP 0. The authors in  also used categories. Table 3. Kolyubin Fig. References Литература 1. Zhu Y. Neurocomputing, , vol. Tabernik D. Chung J. Neural Processing Letters, , in press. Mehta S. Ren S. Faster R-CNN: towards real-time 5. Faster R-CNN: towards real-time object detection with region proposal networks.
Shrivastava A. Training region-based object 6. Training region-based object detectors with online hard example mining. Zaklouta F. Ellahyani A. If a bounding box prior is not assigned, it incurs no classification and localization lost, just confidence loss on objectness. We use tx and ty instead of bx and by to compute the loss. So the range is 0 to 1. The exponential function is likely because we want the bbox to have shape closer to the anchor box. YOLOv3 makes 3 predictions per location.
Each prediction composes of a boundary box, a objectness and 80 class scores, i. To determine the priors, YOLOv3 applies k-means cluster. Then it pre-select 9 clusters. These 9 priors are grouped into 3 different groups according to their scale.
Each group is assigned to a specific feature map above in detecting objects. A new layer Darknet is used to replace the Darknet as the feature extractor. Darknet has less BFLOP billion floating point operations than ResNet, but achieves the same classification accuracy at 2x faster.
YOLOv3 also shows significant improvement in detecting small objects. The problem is the feature map they use has very low resolution and the small object features get too small to be detectable. We assign a negative label to a non-positive anchor if its IoU ratio is lower than 0.
Anchors that are neither positive nor negative do not contribute to the training objective. FPN addresses this multi-scale problem. This is a tradeoff between computation speed and accuracy. The selling point for single shot detector is speed. So they are very cautious about the speed and mAP tradeoff.
In particular, very careful on what complexity may added to the detector. There is a survey paper from Google about how many proposals should make per cell. For regional based detectors, it turns out smaller than some research paper used. It is expensive and therefore they use public dataset like COCO.
The confidence score and objectness is more like a probability rather than an unlimited range number. FPN have 2 path, The bottom-up path is just like the regular CNN which reducing the spatial dimension in extracting features. The top-down is the reverse direction similar to deconvolution. So in YOLO3, in the reverse direction, it goes back 2 layers instead of 1 to generated the feature maps needed for object detection.
If you are very interested in why single shot detector has problems dealing with small objects, the FPN article should explain the issue and propose the solution which YOLOv3 based on. We train the model with the assumption that it has one object per cell. For example, we can label the training data that way and computing the loss function the same.
The NMS described will work if this is more desirable behavior. Otherwise, you can do what you have in mind also. If you look at YOLOv3, they are actually not penalizing some predictions that have high confidence but not the best. In some perspective, from YOLOv1 to v3, the one object per grid is more for the training mechanism. But YOLOv2 switch to anchor box idea too. So the author claim is more a theoretically benefit rather than a solid proof.
I personally suspect one possible reason is that YOLO make fewer predictions. For this fine tuning we train with the above parameters but for only 10 epochs and starting at a learning rate of 10 At this higher resolution our network achieves a top-1 accuracy of So this is slightly different from his claim early in the paper. But this is the implementation detail which may not be very significant in understand the idea. But Redmon is very good at detailing his improvement which I really appreciate.
Many other paper is sometimes very hard to know their implementation details to replicate the result. Паскаль единица давления взаимоисключающие: взаимоисключающие кросс-энтропия: кросс-энтропия, кросс-энтропия среднеквадратичная ошибка: среднеквадратичная ошибка, средняя ошибка, среднеквадратичная ошибка. Пирамида, пирамида vi.
концентрата выходит 1000.
Markets also rely on. Save to Library. Dana L Haynie. Usernames play a major role in online communication by providing a vital first impression and including clues about the identities and personal characteristics of users. Usernames are extremely important in the online trade of illegal Usernames are extremely important in the online trade of illegal drugs, which carries several risks and therefore requires a high degree of trust between the transaction parties.
This study examines how sellers and buyers of illegal drugs represent themselves in their usernames. Data on 1, usernames collected from a Finnish darknet marketplace, Torilauta — are analyzed both qualitatively and quantitatively. The usernames are mostly in Finnish and often feature slang and spoken language expressions. References to various illegal drugs in usernames were found to express a close relationship with the substances. In contrast, they only rarely utilize typical real-life marketing strategies, which suggests that the users do not seek publicity Behind the curtain The illicit trade of firearms, explosives and ammunition on the dark web.
Introduction and problem statement During the last decade, the rapid emergence of novel psychoactive substances NPS has significantly reshaped the Ukrainian drug scene as well as global drug scene. Such changes in the drug scene have The rates and frequencies of new substances and brands appearing in the Ukrainian market are quite impressive although Ukraine has slower rates compared to EU markets.
The NPS phenomenon has induced fundamental changes in legal and illegal drug markets, in national and international drug policies, but — what is most important for Harm Reduction programs — in drug-use subcultures. Methods of analysis The current report has been developed through a desk review of the literature and online resources devoted to the problematics of New Psychoactive Substances, including scientific resources on pharmacodynamics, researches and evidence-based approaches to monitoring side—effects.
The web-resources used vary from strictly academic academia. In addition to the review of scientific literature, non-academic information has been collected from drug user online discussion groups, warnings and alerts about drug-related emergencies, and legislative and policy changes in the last five years.
With increased government surveillance of internet communication, there has been an observable migration of commercial, political and personal activity to the darknet. Journalists wanting to extend their practice into this realm are faced Journalists wanting to extend their practice into this realm are faced with a documented knowledge gap in ethical and practical studies addressing darknet journalism. This research aims at increasing the accessibility of the darknet to journalists by contributing new knowledge towards the formation of a darknet journalism framework.
The study endeavours to achieve this by answering two research questions, the primary one related to the scope of such a framework and the secondary to its contents. Additionally, it hypothesizes that darknet journalism, although presenting significant ethical challenges, can be formulated as a natural extension of online journalism.
Augmented with a set of ten initial practical measures, these represent new knowledge towards the formation of a darknet journalism framework. The results suggest that new and future knowledge, in the form of a framework, may assist journalists in naturally extending themselves into the darknet. Moreover, structural measures, such as enhanced editorial support, heightened awareness for harm, minors and diversity and the formalisation of a darknet journalism specialisation, might contribute towards a deeper integration of the darknet into the general journalistic practice.
It identifies regulatory, policy, and legal challenges, and urges focused policy-maker and legal analyst attention. In contrast, they only rarely utilize typical real-life marketing strategies, which suggests that the users do not seek publicity and do not wish to enhance their online reputation. Overall, it appears that the users aim to blend in with the drug user community rather than stand out from it by building distinctive images of themselves.
Network embeddedness in illegal online markets: endogenous sources of prices and profit in anonymous criminal drug trade. Although economic sociology emphasizes the role of social networks for shaping economic action, little research has examined how network governance structures affect prices in the unregulated and high-risk social context of online Although economic sociology emphasizes the role of social networks for shaping economic action, little research has examined how network governance structures affect prices in the unregulated and high-risk social context of online criminal trade.
We consider how overembeddedness-a state of excessive interconnectedness among market actors-arises from endogenous trade relations to shape prices in illegal online markets with aggregate consequences for short-term gross illegal revenue. Using a series of panel models, we find that increases in closure and repeated exchange raise prices until a threshold is reached upon which prices and gross monthly revenue begin to decline as networks become overembedded. Findings provide insight into the network determinants of prices and gross monthly revenue in illegal online drug trade and illustrate how network structure shapes prices in criminal markets, even in anonymous trade environments.
Research of the Internet as a site for communication and networking has focused mostly on the searchable clear web. The minitrack turns to online darknet environments, an area of the Internet accessed by tools such as onion routing The minitrack turns to online darknet environments, an area of the Internet accessed by tools such as onion routing Tor , and containing anonymous web traffic that cannot be traced by normal means.
As a result, it features a wide range of interests that range from progressive and benevolent to violence and disruption. The Dark Web permits almost unobservable data transfer for, e. How the Dark Web shapes connections and communication and how it intersects with the Clear Web has not been sufficiently explored. Although the usage of the Dark Web is reported in literature, there is a still poor understanding of the Dark Web in terms of its social, cultural, or legal dimensions, to indicate only a few aspects.
Besides, the research is still scarce when it comes to specifics of designing research on the Dark Web, e. This minitrack aim is to give insights and develop a theoretical and practical understanding of issues related to the Dark Web without excluding any methodological approaches. We welcome conceptual, theoretical, empirical and methodological papers that enrich our understanding of Dark Web as a place of communication and networking. It is not a novel fact that social media opens doors for extremist groups in Indonesia.
Several studies and investigations have continuously reported the role of social media and online activism to support terrorist and extremist groups Several studies and investigations have continuously reported the role of social media and online activism to support terrorist and extremist groups for either, general or, interest. Most analysts quickly point out that the most important function of social media is propaganda—posting images, texts, and ideas, to attract people to join or justify their causes, although several reports stress on more specific purposes.
Specyfika handlu narkotykami w polskim darknecie — rekonesans badawczy. The responsiveness of criminal networks to intentional attacks: Disrupting darknet drug trade. Physical, technological, and social networks are often at risk of intentional attack.
Despite the wide-spanning importance of network vulnerability, very little is known about how criminal networks respond to attacks or whether Despite the wide-spanning importance of network vulnerability, very little is known about how criminal networks respond to attacks or whether intentional attacks affect criminal activity in the long-run.
To assess criminal network responsiveness, we designed an empirically-grounded agent-based simulation using population-level network data on 16, illicit drug exchanges between 7, users of an active darknet drug market and statistical methods for simulation analysis. Not in the list? Create a new collection. New collection name:. Parent collection if any : Description optional :. Attached components:. Tick if this dependency is optional.
Task Papers Share Object Detection 27 Usage Over Time. Batch Normalization. Global Average Pooling. Max Pooling. Categories Edit Add Remove. Convolutional Neural Networks. Contact us on: hello paperswithcode. Terms Data policy Cookies policy from. Object Detection.
Find, read and cite all the research you need on ResearchGate. trafﬁc signs detection, deep convolutional neural network, YOLO v3. YOLO neural network. The CNN algorithm considered in this paper has. been built on an open-source platform complied. from the Darknet. Scientiﬁc and Technical Journal of Information Technologies, Keywords trafﬁc signs detection, deep convolutional neural network, YOLO v3.