نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار، دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران
2 استادیار،دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Today, video surveillance systems are widely used to monitor and control the environment. To prevent unwanted incidents and protect military and security sites, people, and their property, investment in video surveillance systems has increased greatly, and the goal is to make the most of all the technological achievements in this field for the development of video surveillance systems. The use of video surveillance systems in organizations, offices, factories, and work environments has resulted in careful monitoring and control of the environment, reducing violations, and increasing the ability to quickly detect incidents and order the work environment. The purpose of identifying and recognizing objects in video surveillance systems is to categorize and label objects and determine their exact position in the image or video.
Today, deep neural networks are used to solve various problems. In this article, we have used the optimized YOLO algorithm to identify and recognize objects in images. In the basic architecture of the YOLO network, the Relo activation function is used, which is not derivable at the zero point, and secondly, it leads to the zeroing of all negative values. As a result, we are trying to increase the accuracy of the basic neural network by changing the activation function. By making a change in the basic architecture of the Yolo algorithm in identifying and recognizing objects, we have increased the accuracy of basic neural networks in identifying and recognizing objects in images by mAP = 0.8%.
کلیدواژهها [English]
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