Abstract
Convolutional Neural Networks (CNNs) have emerged as one of the most powerful tools for object detection and classification tasks in the field of computer vision. This article explores the application of CNNs in detecting and classifying objects in images and video data. It examines the architecture of CNNs, the key challenges in object detection, and the role of various layers in feature extraction. The article also highlights the advancements in CNNs for real-time applications and their integration with other deep learning techniques for improving accuracy and efficiency.
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