Proposed Approach for Object Detection and Recognition by Deep Learning Models Using Data Augmentation

Object detection and recognition play a crucial role in computer vision applications, ranging from security systems to autonomous vehicles. Deep learning algorithms have shown remarkable performance in these tasks, but they often require large, annotated datasets for training. However, collecting such datasets can be time-consuming and costly. Data augmentation techniques provide a solution to this problem by artificially expanding the training dataset. In this study, we propose a deep learning approach for object detection and recognition that leverages data augmentation techniques. We use deep convolutional neural networks (CNNs) as the underlying architecture, specifically focusing on popular models such as You Only Look Once version 3 (YOLOv3). By augmenting the training data with various transformations, such as rotation, scaling, and flipping, we can effectively increase the diversity and size of the dataset. Our approach not only improves the robustness and generalization of the models but also reduces the risk of overfitting. By training on augmented data, the models can learn to recognize objects from different viewpoints, scales, and orientations, leading to improved accuracy and performance. We conduct extensive experiments on benchmark datasets and evaluate the performance of our approach using standard metrics such as precision, recall, and mean average precision (mAP). The experimental results demonstrate that our data augmentation-based deep learning approach achieves superior object detection and recognition accuracy compared to traditional training methods without data augmentation. We compare the average accuracy of the YOLOv3-SPP model with two other variants of the YOLOv3 algorithm: one with a feature extraction network consisting of 53 convolutional layers and the other with 13 convolutional layers. The average accuracy of the proposed model (YOLOv3-SPP) is reported as accuracy of 97%, F1-score of 96%, precision of 94%, and average Intersection over Union (IoU) of 78.04%.