Convolutional Neural Networks for Image Recognition and Object Detection

Abstract

Convolutional Neural Networks, also known as CNNs, have emerged as the most popular deep learning architecture for image recognition and object detection tasks. This is mostly owing to its capacity to automatically learn spatial hierarchies of features from visual data. Through the utilization of local connection, weight sharing, and pooling methods, convolutional neural networks (CNNs) are able to successfully capture patterns such as edges, textures, forms, and object structures. This makes CNNs particularly ideal for comprehensive picture analysis. the importance that CNN architectures play in image identification and object detection, with a particular emphasis on major models like as LeNet, AlexNet, VGG, and ResNet, as well as contemporary detection frameworks such as Faster R-CNN, YOLO, and SSD. The purpose of this article is to investigate how architectural changes can increase the speed of detection, the accuracy of classification, and the extraction of features. Additionally, it highlights issues that are associated with the complexity of computation, the demand for data, and the performance in real time. When compared to more conventional approaches to computer vision, the CNN-based models acquire a higher level of accuracy and robustness through their use. Following the conclusion of the study, the authors highlight the ongoing research areas that are targeted at enhancing the effectiveness, scalability, and interpretability of CNNs for the purpose of practical image recognition and object detection applications.