Object Detection using Convolutional Neural Networks (CNNs): A Study, Implementation and Challenges

Abstract

Detecting objects inside pictures is object recognition method. Object recognition is precarious task in computer vision. Convolutional Neural Networks (CNNs) have object detection systems with significantly advanced the accuracy and efficiency. This paper explores the implementation and challenges of an object recognition model using CNN-based architectures. We review key object detection methods, including various CNN techniques, comparing their performance and suitability for various applications. This study also shows various challenges in Deep learning. The accuracy, precision, and recall is used for to estimate the metrics for good performance of object detection models. Results demonstrate the effectiveness of CNNs in object detection, highlighting both the strengths and challenges of the implemented model. This study suggests possible areas for future research and improvement and also provides insights into the practical application of CNNs in object detection and.