A Novel Approach for Object Detection Technique Using Deep Learning

Summary

This automation era has seen a rapid expansion of the fields of computer vision and image processing. Applications for object detection include face detection, video surveillance, autonomous driving, image retrieval, pedestrian identification, human behavior analysis, and face photo tagging. The task of recognizing and categorizing everything that appears in images or videos is known as object detection (also known as object localization). Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on developing models and algorithms that let computer systems carry out particular functions. Feature extraction and classification are the two primary steps that often comprise machine learning techniques in the context of picture classification. The photos are processed using feature extraction techniques including histogram of oriented gradients (HOG), scale‐invariant feature transform (SIFT), and Haar‐like features to retrieve pertinent data. Classifiers like Adaboost, deformable part models (DPMs), and support vector machines (SVMs) use these attributes to classify the images. There are many reasons why object detection is difficult, such as uneven lighting, occlusions, different placements, small datasets, and imbalances in the classes. Because deep learning techniques were used to further automate the feature extraction procedure, the performance has increased. Issues including occlusions, position and perspective changes, object rotation and scale changes, different object sizes, distortion, and intraclass variance remain unresolved even though convolutional neural networks (CNNs) are currently being researched. In different applications, object detection algorithms are impacted by each of these aspects. The most recent developments in Deep Learning techniques for object detection and identification are examined in this article. These developments have enormous promise for the fields of image processing and computer vision. The goal of this study is to present various methods that, when compared to the existing approaches, may perform Object Detection with a significantly greater level of accuracy and reasonable real‐time performance. One of the primary problems with object recognition is image classification, or predicting the class of a picture. When an image contains an object collection, image localization becomes a little trickier to solve. The class and location of the objects throughout the image should be the system's prediction in this instance. The most difficult aspect of the object detection task is combining localization and categorization. The present study only considers the VGG‐16 model, despite the fact that there are several deep learning models for object detection accessible in the literature. The study that is now being published indicates that for more complex applications, the accuracy of these models in detecting objects needs to be further improved.

Therefore, the current research effort aims to achieve a significant improvement in object detection accuracy above the current levels. This study starts with the fundamental ideas of deep learning before delving further into various deep learning architectures. The four hyper‐parameters that affect object detection accuracy—”learning rate,” “epoch rate,” “training batch size,” and “testing batch size”—are the subject of the current work. The learning rate of a model is the speed at which it chooses weights from the neural network.