Intelligent Deep Learning and Improved Whale Optimization Algorithm Based Framework for Object Recognition

In pattern recognition, object recognition is an important research domain due to major applications such as autonomous driving, robotics, and visual surveillance. Many computer vision techniques are introduced in the literature. Several challenges exist, such as similar shapes of different objects and imbalanced datasets. They also face irrelevant feature extraction, which degrades the recognition accuracy and increases the computational time. In this article, we proposed a fully automated computer vision pipeline for object recognition. In the proposed method, initially perform the data augmentation to balance the object classes. In the later step, a convolutional neural network (DenseNet201) was considered and modified according to the selected dataset (Caltech101). The modified model is trained by transfer learning and extracts features. The extracted features include a few redundant information removed using an improved whale optimization algorithm (WOA). Final features are classified using several supervised learning algorithms for final recognition. The experimental process was carried out using the augmented Caltech101 dataset and accomplished an accuracy of 93%.