In recent years, object detection (OD) has become essential in computer vision for identifying and localizing objects in digital images, prompting various sectors to adopt this technology. However, increased reliance ...
详细信息
In recent years, object detection (OD) has become essential in computer vision for identifying and localizing objects in digital images, prompting various sectors to adopt this technology. However, increased reliance on OD has also revealed vulnerabilities to attacks, highlighting the need for effective detection methods to mitigate potential risks. Therefore, the present paper primarily surveys existing studies on OD in the context of security and surveillance, highlighting its significance in these critical areas. The discussion includes an examination of conventional techniques such as HOG, DPM, and the Viola‒Jones detector. While these traditional methods have laid the groundwork for object detection, they are often considered inadequate because of their time-consuming and labor-intensive nature. Consequently, the focus shifts to DL (deep learning)-based OD models such as YOLO (you only look once), single shot detector (SSD), and Fast R-CNN. Among these, the present survey paper emphasizes YOLO models for their speed and efficiency, as they utilize a unified architecture for both region proposal and classification, making them particularly suitable for real-time applications. However, the distinguishing feature of the proposed survey lies in its comprehensive coverage, which not only encompasses YOLO models but also integrates an analysis of generative AI (GenAI) models and metaheuristic approaches. This multifaceted exploration allows for a richer understanding of the current landscape in computer vision and AI, highlighting the synergies and potential applications that arise from combining these diverse methodologies. Furthermore, the paper explores a wide range of applications for OD in real-time security and surveillance settings, illustrating its effectiveness in addressing contemporary security challenges. This highlights how advanced OD techniques can enhance situational awareness and response capabilities in various scenarios. By focusing on these aspect
The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thu...
详细信息
Internet of Things (IoT) devices are often directly authenticated by the gateways within the network. In complex and large systems, IoT devices may be connected to the gateway through another device in the network. In...
详细信息
In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these chal...
详细信息
Image caption-generating systems aim to deliver accurate, coherent, and useful captions. This includes identifying the scene, items, relationships, and attributes of the image's objects. Due to constraints in usin...
详细信息
Skeletal muscle ultrasound has emerged as a pivotal imaging modality in rheumatology clinics, offering unparalleled advantages such as radiation-free imaging, safety, and dynamic examination capabilities. However, its...
详细信息
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws...
详细信息
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the infor...
详细信息
Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dy...
详细信息
Effective task scheduling and resource allocation have become major problems as a result of the fast development of cloud computing as well as the rise of multi-cloud systems. To successfully handle these issues, we p...
详细信息
暂无评论