Gotowa bibliografia na temat „You only look once version 5”

This project aims to develop a vision system that can detect traffic light counter and to recognise the numbers shown on it. The system used you only look once version 3 (YOLOv3) algorithm because of its robust performance and reliability and able to be implemented in Nvidia Jetson nano kit. A total of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another 80% (1764) from the images are used for training and 20% (440) are used for testing. The results obtained from the training demonstrated Total precision=89%, Recall=99.2%, F1 score=70%, intersection over union (IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2% and the estimate total confidence rate for red and green are 98.4% and 99.3% respectively. The results were compared with the previous YOLOv5 algorithm, and the results are substantially close to each other as the YOLOv5 accuracy and recall at 97.5% and 97.5% respectively.

<p>This project aims to develop a vision system that can detect traffic light<br />counter and to recognise the numbers shown on it. The system used you only<br />look once version 3 (YOLOv3) algorithm because of its robust performance<br />and reliability and able to be implemented in Nvidia Jetson nano kit. A total<br />of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another<br />80% (1764) from the images are used for training and 20% (440) are used for<br />testing. The results obtained from the training demonstrated Total<br />precision=89%, Recall=99.2%, F1 score=70%, intersection over union<br />(IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2%<br />and the estimate total confidence rate for red and green are 98.4% and 99.3%<br />respectively. The results were compared with the previous YOLOv5<br />algorithm, and the results are substantially close to each other as the YOLOv5<br />accuracy and recall at 97.5% and 97.5% respectively.</p>

Deep neural networks are currently employed to detect weapons, and although these techniques provide a high level of accuracy, it still suffers from large weight parameters and a slow inference speed. When considering real-world applications like weapon detection, these methods are frequently unsuitable for deployment on embedded devices due to their large number of parameters and poor efficiency. The most recent object detection technique, which falls under the YOLOv5 (You Only Look Once version 5) family, is commonly used for detecting weapons. However, it faces some difficulties such as high computational parameters and an unfavorable detection rate. to solve these shortcomings. an enhanced lightweight Yolov5s approach is suggested. Which consists of a combination of YOLOv5 and GhostNet modules. To evaluate the efficacy of the suggested technique, a set of experiments was performed on the Sohas weapon dataset., which is commonly used as a reference dataset in the field. Compared to the original YOLOv5, the results indicate a slight increase in the proposed model's mean Average Precision (mAP). Furthermore, there has been a reduction of 2.7 in giga floating point operations per second (GFLOPs) and weights, and the number of model parameters has decreased by 1.42.

With the development of virtual reality, the human–computer interaction through virtual sports is gradually maturing, and users are gradually looking to interact with the two-dimensional world. The research on this type of algorithm has gained attention. However, due to the delay of the old transmission technology in the transmission of pictures, which is higher than the reaction time of human brain, the pictures are inconsistent and illogical, and the user interaction experience is poor. To solve it, this research realizes the fusion of ghost network and You Only Look Once version 5, and the simulation experiment is carried out on the data set. Firstly, the convolution block attention module is inserted into the You Only Look Once version 5 algorithm to optimize its way of calculating Hadamard product. Then, the improved algorithm and the ghost network generation fusion algorithm are combined through the direct channel. Then the algorithm is combined with the virtual sports interactive system to upgrade its key point rearrangement mode. Finally, the performance of the system is characterized on Javelin dataset, and the stability is compared with the other three algorithms. The average score of the six experiments of the system is 9.5, and the average scores of You Only Look Once version 5, ghost network and particle swarm optimization algorithm are 9.42, 9.28 and 9.36, respectively. Results show that this model has excellent performance in adjusting data volatility, and is extensive in virtual sports interaction, which can effectively improve the user experience.

You Only Look Once (YOLO) series detectors are suitable for aerial image object detection because of their excellent real-time ability and performance. Their high performance depends heavily on the anchor generated by clustering the training set. However, the effectiveness of the general Anchor Generation algorithm is limited by the unique data distribution of the aerial image dataset. The divergence in the distribution of the number of objects with different sizes can cause the anchors to overfit some objects or be assigned to suboptimal layers because anchors of each layer are generated uniformly and affected by the overall data distribution. In this paper, we are inspired by experiments under different anchors settings and proposed the Layered Anchor Generation (LAG) algorithm. In the LAG, objects are layered by their diagonals, and then anchors of each layer are generated by analyzing the diagonals and aspect ratio of objects of the corresponding layer. In this way, anchors of each layer can better match the detection range of each layer. Experiment results showed that our algorithm is of good generality that significantly uprises the performance of You Only Look Once version 3 (YOLOv3), You Only Look Once version 5 (YOLOv5), You Only Learn One Representation (YOLOR), and Cascade Regions with CNN features (Cascade R-CNN) on the Vision Meets Drone (VisDrone) dataset and the object DetectIon in Optical Remote sensing images (DIOR) dataset, and these improvements are cost-free.

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities.

More and more information on tourist spots is being represented as pictures rather than text. Consequently, tourists who are interested in a specific attraction shown in pictures may have no idea how to perform a text search to get more information about the interesting tourist spots. In the view of this problem and to enhance the competitiveness of the tourism market, this research proposes an innovative tourist spot identification mechanism, which is based on deep learning-based object detection technology, for real-time detection and identification of tourist spots by taking pictures on location or retrieving images from the Internet. This research establishes a tourist spot recognition system, which is a You Only Look Once version 3 model built in Tensorflow AI framework, and is used to identify tourist attractions by taking pictures with a smartphone's camera. To verify the possibility, a set of tourist spots in Hsinchu City, Taiwan is taken as an example. Currently, the tourist spot recognition system of this research can identify 28 tourist spots in Hsinchu. In addition to the attraction recognition feature, tourists can further use this tourist spot recognition system to obtain more information about 77 tourist spots from the Hsinchu City Government Information Open Data Platform, and then make dynamic travel itinerary planning and Google MAP navigation. Compared with other deep learning models using Faster region-convolutional neural networks or Single-Shot Multibox Detector algorithms for the same data set, the recognition time by the models using You Only Look Once version 3, Faster region-convolutional neural networks, and Single-Shot Multibox Detector algorithms are respectively 4.5, 5, and 9 s, and the mean average precision for each when IoU = 0.6 is 88.63%, 85%, and 43.19%, respectively. The performance experimental results of this research show the model using the You Only Look Once version 3 algorithm is more efficient and precise than the models using the Faster region-convolutional neural networks or the Single-Shot Multibox Detector algorithms, where You Only Look Once version 3 and Single-Shot Multibox Detector are one-stage learning architectures with efficient features, and Faster region-convolutional neural networks is a two-stage learning architecture with precise features.

Nationalism: Past as Prologue began as a single volume being compiled by Ad Akande, a scholar from South Africa, who proposed it to me as co-author about two years ago. The original idea was to examine how the damaging roots of nationalism have been corroding political systems around the world, and creating dangerous obstacles for necessary international cooperation. Since I (Bruce E. Johansen) has written profusely about climate change (global warming, a.k.a. infrared forcing), I suggested a concerted effort in that direction. This is a worldwide existential threat that affects every living thing on Earth. It often compounds upon itself, so delays in reducing emissions of fossil fuels are shortening the amount of time remaining to eliminate the use of fossil fuels to preserve a livable planet. Nationalism often impedes solutions to this problem (among many others), as nations place their singular needs above the common good. Our initial proposal got around, and abstracts on many subjects arrived. Within a few weeks, we had enough good material for a 100,000-word book. The book then fattened to two moderate volumes and then to four two very hefty tomes. We tried several different titles as good submissions swelled. We also discovered that our best contributors were experts in their fields, which ranged the world. We settled on three stand-alone books:” 1/ nationalism and racial justice. Our first volume grew as the growth of Black Lives Matter following the brutal killing of George Floyd ignited protests over police brutality and other issues during 2020, following the police assassination of Floyd in Minneapolis. It is estimated that more people took part in protests of police brutality during the summer of 2020 than any other series of marches in United States history. This includes upheavals during the 1960s over racial issues and against the war in Southeast Asia (notably Vietnam). We choose a volume on racism because it is one of nationalism’s main motive forces. This volume provides a worldwide array of work on nationalism’s growth in various countries, usually by authors residing in them, or in the United States with ethnic ties to the nation being examined, often recent immigrants to the United States from them. Our roster of contributors comprises a small United Nations of insightful, well-written research and commentary from Indonesia, New Zealand, Australia, China, India, South Africa, France, Portugal, Estonia, Hungary, Russia, Poland, Kazakhstan, Georgia, and the United States. Volume 2 (this one) describes and analyzes nationalism, by country, around the world, except for the United States; and 3/material directly related to President Donald Trump, and the United States. The first volume is under consideration at the Texas A & M University Press. The other two are under contract to Nova Science Publishers (which includes social sciences). These three volumes may be used individually or as a set. Environmental material is taken up in appropriate places in each of the three books. * * * * * What became the United States of America has been strongly nationalist since the English of present-day Massachusetts and Jamestown first hit North America’s eastern shores. The country propelled itself across North America with the self-serving ideology of “manifest destiny” for four centuries before Donald Trump came along. Anyone who believes that a Trumpian affection for deportation of “illegals” is a new thing ought to take a look at immigration and deportation statistics in Adam Goodman’s The Deportation Machine: America’s Long History of Deporting Immigrants (Princeton University Press, 2020). Between 1920 and 2018, the United States deported 56.3 million people, compared with 51.7 million who were granted legal immigration status during the same dates. Nearly nine of ten deportees were Mexican (Nolan, 2020, 83). This kind of nationalism, has become an assassin of democracy as well as an impediment to solving global problems. Paul Krugman wrote in the New York Times (2019:A-25): that “In their 2018 book, How Democracies Die, the political scientists Steven Levitsky and Daniel Ziblatt documented how this process has played out in many countries, from Vladimir Putin’s Russia, to Recep Erdogan’s Turkey, to Viktor Orban’s Hungary. Add to these India’s Narendra Modi, China’s Xi Jinping, and the United States’ Donald Trump, among others. Bit by bit, the guardrails of democracy have been torn down, as institutions meant to serve the public became tools of ruling parties and self-serving ideologies, weaponized to punish and intimidate opposition parties’ opponents. On paper, these countries are still democracies; in practice, they have become one-party regimes….And it’s happening here [the United States] as we speak. If you are not worried about the future of American democracy, you aren’t paying attention” (Krugmam, 2019, A-25). We are reminded continuously that the late Carl Sagan, one of our most insightful scientific public intellectuals, had an interesting theory about highly developed civilizations. Given the number of stars and planets that must exist in the vast reaches of the universe, he said, there must be other highly developed and organized forms of life. Distance may keep us from making physical contact, but Sagan said that another reason we may never be on speaking terms with another intelligent race is (judging from our own example) could be their penchant for destroying themselves in relatively short order after reaching technological complexity. This book’s chapters, introduction, and conclusion examine the worldwide rise of partisan nationalism and the damage it has wrought on the worldwide pursuit of solutions for issues requiring worldwide scope, such scientific co-operation public health and others, mixing analysis of both. We use both historical description and analysis. This analysis concludes with a description of why we must avoid the isolating nature of nationalism that isolates people and encourages separation if we are to deal with issues of world-wide concern, and to maintain a sustainable, survivable Earth, placing the dominant political movement of our time against the Earth’s existential crises. Our contributors, all experts in their fields, each have assumed responsibility for a country, or two if they are related. This work entwines themes of worldwide concern with the political growth of nationalism because leaders with such a worldview are disinclined to co-operate internationally at a time when nations must find ways to solve common problems, such as the climate crisis. Inability to cooperate at this stage may doom everyone, eventually, to an overheated, stormy future plagued by droughts and deluges portending shortages of food and other essential commodities, meanwhile destroying large coastal urban areas because of rising sea levels. Future historians may look back at our time and wonder why as well as how our world succumbed to isolating nationalism at a time when time was so short for cooperative intervention which is crucial for survival of a sustainable earth. Pride in language and culture is salubrious to individuals’ sense of history and identity. Excess nationalism that prevents international co-operation on harmful worldwide maladies is quite another. As Pope Francis has pointed out: For all of our connectivity due to expansion of social media, ability to communicate can breed contempt as well as mutual trust. “For all our hyper-connectivity,” said Francis, “We witnessed a fragmentation that made it more difficult to resolve problems that affect us all” (Horowitz, 2020, A-12). The pope’s encyclical, titled “Brothers All,” also said: “The forces of myopic, extremist, resentful, and aggressive nationalism are on the rise.” The pope’s document also advocates support for migrants, as well as resistance to nationalist and tribal populism. Francis broadened his critique to the role of market capitalism, as well as nationalism has failed the peoples of the world when they need co-operation and solidarity in the face of the world-wide corona virus pandemic. Humankind needs to unite into “a new sense of the human family [Fratelli Tutti, “Brothers All”], that rejects war at all costs” (Pope, 2020, 6-A). Our journey takes us first to Russia, with the able eye and honed expertise of Richard D. Anderson, Jr. who teaches as UCLA and publishes on the subject of his chapter: “Putin, Russian identity, and Russia’s conduct at home and abroad.” Readers should find Dr. Anderson’s analysis fascinating because Vladimir Putin, the singular leader of Russian foreign and domestic policy these days (and perhaps for the rest of his life, given how malleable Russia’s Constitution has become) may be a short man physically, but has high ambitions. One of these involves restoring the old Russian (and Soviet) empire, which would involve re-subjugating a number of nations that broke off as the old order dissolved about 30 years ago. President (shall we say czar?) Putin also has international ambitions, notably by destabilizing the United States, where election meddling has become a specialty. The sight of Putin and U.S. president Donald Trump, two very rich men (Putin $70-$200 billion; Trump $2.5 billion), nuzzling in friendship would probably set Thomas Jefferson and Vladimir Lenin spinning in their graves. The road of history can take some unanticipated twists and turns. Consider Poland, from which we have an expert native analysis in chapter 2, Bartosz Hlebowicz, who is a Polish anthropologist and journalist. His piece is titled “Lawless and Unjust: How to Quickly Make Your Own Country a Puppet State Run by a Group of Hoodlums – the Hopeless Case of Poland (2015–2020).” When I visited Poland to teach and lecture twice between 2006 and 2008, most people seemed to be walking on air induced by freedom to conduct their own affairs to an unusual degree for a state usually squeezed between nationalists in Germany and Russia. What did the Poles then do in a couple of decades? Read Hlebowicz’ chapter and decide. It certainly isn’t soft-bellied liberalism. In Chapter 3, with Bruce E. Johansen, we visit China’s western provinces, the lands of Tibet as well as the Uighurs and other Muslims in the Xinjiang region, who would most assuredly resent being characterized as being possessed by the Chinese of the Han to the east. As a student of Native American history, I had never before thought of the Tibetans and Uighurs as Native peoples struggling against the Independence-minded peoples of a land that is called an adjunct of China on most of our maps. The random act of sitting next to a young woman on an Air India flight out of Hyderabad, bound for New Delhi taught me that the Tibetans had something to share with the Lakota, the Iroquois, and hundreds of other Native American states and nations in North America. Active resistance to Chinese rule lasted into the mid-nineteenth century, and continues today in a subversive manner, even in song, as I learned in 2018 when I acted as a foreign adjudicator on a Ph.D. dissertation by a Tibetan student at the University of Madras (in what is now in a city called Chennai), in southwestern India on resistance in song during Tibet’s recent history. Tibet is one of very few places on Earth where a young dissident can get shot to death for singing a song that troubles China’s Quest for Lebensraum. The situation in Xinjiang region, where close to a million Muslims have been interned in “reeducation” camps surrounded with brick walls and barbed wire. They sing, too. Come with us and hear the music. Back to Europe now, in Chapter 4, to Portugal and Spain, we find a break in the general pattern of nationalism. Portugal has been more progressive governmentally than most. Spain varies from a liberal majority to military coups, a pattern which has been exported to Latin America. A situation such as this can make use of the term “populism” problematic, because general usage in our time usually ties the word into a right-wing connotative straightjacket. “Populism” can be used to describe progressive (left-wing) insurgencies as well. José Pinto, who is native to Portugal and also researches and writes in Spanish as well as English, in “Populism in Portugal and Spain: a Real Neighbourhood?” provides insight into these historical paradoxes. Hungary shares some historical inclinations with Poland (above). Both emerged from Soviet dominance in an air of developing freedom and multicultural diversity after the Berlin Wall fell and the Soviet Union collapsed. Then, gradually at first, right wing-forces began to tighten up, stripping structures supporting popular freedom, from the courts, mass media, and other institutions. In Chapter 5, Bernard Tamas, in “From Youth Movement to Right-Liberal Wing Authoritarianism: The Rise of Fidesz and the Decline of Hungarian Democracy” puts the renewed growth of political and social repression into a context of worldwide nationalism. Tamas, an associate professor of political science at Valdosta State University, has been a postdoctoral fellow at Harvard University and a Fulbright scholar at the Central European University in Budapest, Hungary. His books include From Dissident to Party Politics: The Struggle for Democracy in Post-Communist Hungary (2007). Bear in mind that not everyone shares Orbán’s vision of what will make this nation great, again. On graffiti-covered walls in Budapest, Runes (traditional Hungarian script) has been found that read “Orbán is a motherfucker” (Mikanowski, 2019, 58). Also in Europe, in Chapter 6, Professor Ronan Le Coadic, of the University of Rennes, Rennes, France, in “Is There a Revival of French Nationalism?” Stating this title in the form of a question is quite appropriate because France’s nationalistic shift has built and ebbed several times during the last few decades. For a time after 2000, it came close to assuming the role of a substantial minority, only to ebb after that. In 2017, the candidate of the National Front reached the second round of the French presidential election. This was the second time this nationalist party reached the second round of the presidential election in the history of the Fifth Republic. In 2002, however, Jean-Marie Le Pen had only obtained 17.79% of the votes, while fifteen years later his daughter, Marine Le Pen, almost doubled her father's record, reaching 33.90% of the votes cast. Moreover, in the 2019 European elections, re-named Rassemblement National obtained the largest number of votes of all French political formations and can therefore boast of being "the leading party in France.” The brutality of oppressive nationalism may be expressed in personal relationships, such as child abuse. While Indonesia and Aotearoa [the Maoris’ name for New Zealand] hold very different ranks in the United Nations Human Development Programme assessments, where Indonesia is classified as a medium development country and Aotearoa New Zealand as a very high development country. In Chapter 7, “Domestic Violence Against Women in Indonesia and Aotearoa New Zealand: Making Sense of Differences and Similarities” co-authors, in Chapter 8, Mandy Morgan and Dr. Elli N. Hayati, from New Zealand and Indonesia respectively, found that despite their socio-economic differences, one in three women in each country experience physical or sexual intimate partner violence over their lifetime. In this chapter ther authors aim to deepen understandings of domestic violence through discussion of the socio-economic and demographic characteristics of theit countries to address domestic violence alongside studies of women’s attitudes to gender norms and experiences of intimate partner violence. One of the most surprising and upsetting scholarly journeys that a North American student may take involves Adolf Hitler’s comments on oppression of American Indians and Blacks as he imagined the construction of the Nazi state, a genesis of nationalism that is all but unknown in the United States of America, traced in this volume (Chapter 8) by co-editor Johansen. Beginning in Mein Kampf, during the 1920s, Hitler explicitly used the westward expansion of the United States across North America as a model and justification for Nazi conquest and anticipated colonization by Germans of what the Nazis called the “wild East” – the Slavic nations of Poland, the Baltic states, Ukraine, and Russia, most of which were under control of the Soviet Union. The Volga River (in Russia) was styled by Hitler as the Germans’ Mississippi, and covered wagons were readied for the German “manifest destiny” of imprisoning, eradicating, and replacing peoples the Nazis deemed inferior, all with direct references to events in North America during the previous century. At the same time, with no sense of contradiction, the Nazis partook of a long-standing German romanticism of Native Americans. One of Goebbels’ less propitious schemes was to confer honorary Aryan status on Native American tribes, in the hope that they would rise up against their oppressors. U.S. racial attitudes were “evidence [to the Nazis] that America was evolving in the right direction, despite its specious rhetoric about equality.” Ming Xie, originally from Beijing, in the People’s Republic of China, in Chapter 9, “News Coverage and Public Perceptions of the Social Credit System in China,” writes that The State Council of China in 2014 announced “that a nationwide social credit system would be established” in China. “Under this system, individuals, private companies, social organizations, and governmental agencies are assigned a score which will be calculated based on their trustworthiness and daily actions such as transaction history, professional conduct, obedience to law, corruption, tax evasion, and academic plagiarism.” The “nationalism” in this case is that of the state over the individual. China has 1.4 billion people; this system takes their measure for the purpose of state control. Once fully operational, control will be more subtle. People who are subject to it, through modern technology (most often smart phones) will prompt many people to self-censor. Orwell, modernized, might write: “Your smart phone is watching you.” Ming Xie holds two Ph.Ds, one in Public Administration from University of Nebraska at Omaha and another in Cultural Anthropology from the Chinese Academy of Social Sciences, Beijing, where she also worked for more than 10 years at a national think tank in the same institution. While there she summarized news from non-Chinese sources for senior members of the Chinese Communist Party. Ming is presently an assistant professor at the Department of Political Science and Criminal Justice, West Texas A&M University. In Chapter 10, analyzing native peoples and nationhood, Barbara Alice Mann, Professor of Honours at the University of Toledo, in “Divide, et Impera: The Self-Genocide Game” details ways in which European-American invaders deprive the conquered of their sense of nationhood as part of a subjugation system that amounts to genocide, rubbing out their languages and cultures -- and ultimately forcing the native peoples to assimilate on their own, for survival in a culture that is foreign to them. Mann is one of Native American Studies’ most acute critics of conquests’ contradictions, and an author who retrieves Native history with a powerful sense of voice and purpose, having authored roughly a dozen books and numerous book chapters, among many other works, who has traveled around the world lecturing and publishing on many subjects. Nalanda Roy and S. Mae Pedron in Chapter 11, “Understanding the Face of Humanity: The Rohingya Genocide.” describe one of the largest forced migrations in the history of the human race, the removal of 700,000 to 800,000 Muslims from Buddhist Myanmar to Bangladesh, which itself is already one of the most crowded and impoverished nations on Earth. With about 150 million people packed into an area the size of Nebraska and Iowa (population less than a tenth that of Bangladesh, a country that is losing land steadily to rising sea levels and erosion of the Ganges river delta. The Rohingyas’ refugee camp has been squeezed onto a gigantic, eroding, muddy slope that contains nearly no vegetation. However, Bangladesh is majority Muslim, so while the Rohingya may starve, they won’t be shot to death by marauding armies. Both authors of this exquisite (and excruciating) account teach at Georgia Southern University in Savannah, Georgia, Roy as an associate professor of International Studies and Asian politics, and Pedron as a graduate student; Roy originally hails from very eastern India, close to both Myanmar and Bangladesh, so he has special insight into the context of one of the most brutal genocides of our time, or any other. This is our case describing the problems that nationalism has and will pose for the sustainability of the Earth as our little blue-and-green orb becomes more crowded over time. The old ways, in which national arguments often end in devastating wars, are obsolete, given that the Earth and all the people, plants, and other animals that it sustains are faced with the existential threat of a climate crisis that within two centuries, more or less, will flood large parts of coastal cities, and endanger many species of plants and animals. To survive, we must listen to the Earth, and observe her travails, because they are increasingly our own.

Abstract The combination of computer vision and deep learning approaches has changed automated systems across numerous domains. Such a domain is object detection. This study presents an automatic boom gate access method based on the YOLOv8 (You Only Look Once version 8) object detection model and license plate recognition (LPR) technology. It tries to resolve the issue of secure and efficient boom gate entry in restricted regions. The approach takes advantage of YOLOv8’s capacity to reliably detect and recognize license plates in real-time, allowing for automated gate operation. The experimental approach on the YOLOv8 framework used an open-sourced vehicle plate number dataset, to train and test the model, thereby reporting an average precision of 80%, a Mean Average Precision (mAP) of 76% at a threshold of 50%, and an average F-1 score of 75%. Experimental tests with live samples demonstrated how well and consistently the proposed technique performs in a variety of environmental situations, including lighting, weather, and vehicle orientations. The findings demonstrated the YOLOv8-based system’s effective application in real-world circumstances.

Abstract Introduction An introductory address, which once headed the written version of the Orations (see Introduction, p. lix), and could have been delivered viva voce on any number of separate occasions. In it, Maximus seeks to persuade his audience both that they need philosophical instruction, and that he is the man to provide it. §§ r- 5 develop the first point: philosophical teaching is essential to bring order and stability into the confusion of human life, by directing humanity to the only truly satisfying and worthwhile goal (Virtue); this goal is both practically attainable, and endorsed by the best Greek traditions; in order to realize it, philosophical teaching itself will take many different forms, and present itself on many different occasions. An element of implicit self-advertisement and self-justification is already detectable in this: ‘do not look for your philosophical instruction only in a narrowly defined set of situations; occasions like this have their part to play too’. In § § 6-ro the advertisement becomes open and direct, as Maximus simultaneously boasts of his oratorical skill and challenges his audience to react to his moral message instead. At the same time he seeks to reassure them that his and their (high) social status is no barrier to claiming the title ‘philosopher’: impressions to the contrary spring from an ignorant misreading of the historical record (§ § 9-ro).

To help the management of stores, especially the stores that provide self-check digital services, and to better understand and study the object-detection algorithm based on You Only Look Once, used many ways to train and get a YOLO model that satisfy the need of detecting multiple products in images. The RPC dataset is used to train the model, part of which is used in the training process to generate a final version, the result performance of the model generally shows the YOLO algorithm has its strengths when faced with images of retail goods.

Sign language is a terminology that encloses a motion of hand gestures which is an environment for the auditory impairment, individual (deaf or dumb) to deal with others. Nevertheless, so as to impart with the hearing impaired individual, the communicator obtains to acquire acquaintance in sign language. As follows is frequent to make undoubted that the message provided by the hearing impaired person acknowledged. This implemented system propounds an implementation of real time American Sign Language perception in Convolutional Neural Network (CNN) with the support of You Only Look Once version (YOLO) algorithm. The algorithm initially executes data acquisition, subsequently the pre-processing of gestures and are conducted to trace hand movement utilize a combinational algorithm.

Brain-tumor detection is a very complicated task in the medical domain. The early detection of tumors in patients' lives can provide improved outcomes and a balanced life. Magnetic Resonance Imaging (MRI) is an essential tool in the domain of medical diagnostics, particularly for tumor detection. The application of advanced image segmentation algorithms, such as the YOLOv7 (You Only Look Once version 7), has shown promising outcomes in enhancing the accuracy as well as efficiency of tumor detection. This chapter explores the performance optimization of MRI images for tumor detection using YOLOv7, providing a practical approach to implementation and analysis. The proposed approach implements a CNN model to classify the types of tumors and YOLOv7 for the detection of tumor. After training, the mean average precision is 98% to correctly identify the brain-tumor in a particular segment of the brain along with its types.

The interdisciplinary convergence of computer vision and object detection is pivotal for advancing intelligent image analysis. This research surpasses conventional object recognition methodologies by delving into a more nuanced understanding of images, akin to human visual comprehension. It explores deep learning and established object detection systems such as convolutional neural networks (CNN), Region-based CNN (R-CNN), and you only look once (YOLO). The proposed model excels in realtime object recognition, outperforming its predecessors, as previous systems typically detect only a limited number of objects in an image and are most effective at a distance of 5-6 meters. Uniquely, it employs Google Translate for the verbal identification of detected objects, offering a crucial accessibility feature for individuals with visual impairments. This study integrates computer vision, deep learning, and real-time object recognition to enhance visual perception, providing valuable assistance to those facing visual challenges. The proposed method utilizes the Common Objects in Context (COCO) dataset for image comprehension, employing object detection and object tracking with a deep neural network (DNN). The system's output is converted into spoken words through a text-to-speech feature, empowering visually impaired individuals to comprehend their surroundings effectively. The implementation involves key technologies such as NumPy, OpenCV, pyttsx3, PyWin32, OpenCV-contribpython, and winsound, contributing to a comprehensive system for computer vision and audio processing. Results demonstrate successful execution, with the camera consistently detecting and labeling 5-6 objects in real time.

In this chapter, we will create a program that randomly generates pitches at a specified tempo. The program will have the ability to change a number of musical variables including timbre, velocity, and tempo. We will also write a program that allows your MIDI keyboard to function as a synthesizer. These two programs will be the basis of future projects related to composition and performance. Since you’ve already learned a number of objects in the previous chapter, let’s agree that when you’re asked to create an object that you already know, like button, for example, it will be sufficient for me to say “create a button” instead of repeating the process of creating a new object box and typing in the word button. Combining steps in this way will help us to get through the instructions with greater speed while reinforcing your understanding of how certain objects work. In this way, the instruction “create a message box containing the numbers 41 and 38” actually combines several smaller, and hopefully intuitive, instructions into a single step. I will slowly stop mentioning key commands and other shortcuts for objects and tasks that I’ve already introduced. Create a new patch and 1. Create a new object (press n) called random The random object takes a number as its only argument and randomly generates a number between 0 and one less than the argument when it receives a bang in its inlet. 2. Give this random object the argument 128 (Note: if you already clicked away from the object, double click it in order to, once again, enable typing within the object box) Be sure to put a space between the word random and the argument 128 or else Max will look for an object called random128 that does not exist. 3. Create a new button (press b) 4. Connect the outlet of button to the first inlet of random 128 5. Create a number box (press i) 6. Connect the outlet of random 128 to the inlet of the number box

Intersex individuals are coming out of the closet. Witness, for example, the 2003 Pulitzer Prize in Fiction for Jeffrey Eugenides’s Middlesex. The story follows someone with 5-alpha-reductase deficiency, or late-onset virilization. Imagine yourself raised as a girl, discovering at puberty (through cryptic, piecemeal clues) that you are male instead. Or male also? Or male only now? Or “just” newly virile? The condition confounds the conventionally strict dichotomy between male and female, masculine and feminine. It teases a culture preoccupied with gender. What are male and female, biologically? How does nature define the sexes, and sex itself? The questions seem simple enough. Seeking answers, however, may yield unexpected lessons—about the role of biological definitions; about assumptions concerning universals, rarities, and “normality”; and about the power of mistaken conceptions of nature to shape culture. Conceptualizing sex as male and female seems straightforward. In the standard version (familiar even to those unschooled in biology), females have two X chromosomes, while males have an X and a Y. They have different gametes: one moves, one stays stationary. These differences seem foundational. They seem to explain why male and female organisms have contrasting gonads, contrasting hormone-mediated physiologies, and contrasting secondary sex characteristics. Once-homologous organs follow divergent developmental trajectories. Perhaps even contrasting behaviors express the purported evolutionary imperative of each gamete: the “promiscuous,” uncaring male of cheap sperm, and the cunning, protective female of big-investment eggs. The apparent alignment of the two sexes through all levels of biological organization seems to validate this categorization as scientifically sound. Good biologists know better. First, sex may be determined in many ways. Birds use a “reversed,” WZ system, where females have the distinctive chromosome. Many insects have a haplodiploid system, where sex is determined by having a single or double set of all the chromosomes. Crocodiles and turtles develop their sex in response to temperature cues, not genes alone. The spoonworm Bonellia responds instead to whether females are absent or already present in the area.