Utilizing Convolutional Neural Networks for Object Recognition with Image Filtering and Edge Detection in Deep Learning

The optimal technique for object detection directly in the compressed domain of a video sequence or an image is presented in this study, we delve into the application of Convolutional Neural Networks (CNNs) for object recognition, employing image filtering and edge detection techniques within the framework of deep learning. The paper leverages the power of CNNs to automatically learn and extract essential features from images, enabling effective object recognition. We explore the significance of image filtering and edge detection as preprocessing steps to enhance feature extraction, leading to improved classification accuracy. Through comprehensive experiments and analyses, we demonstrate the efficacy of our approach in various real-world scenarios, highlighting the potential for CNNs to play a pivotal role in advancing object recognition technology. This paper offers valuable insights into the synergy between CNNs and image processing techniques, with promising implications for diverse applications, including computer vision, robotics, and autonomous systems.