A Comprehensive Review of Object Detection Using Deep Learning

Abstract: In recent times, deep learning has emerged as one of the powerful tools in the process of object detection. The deep learning algorithms that are used in object detection are constructive in both localization and classification of objects that are in the videos or the images. The technique automatically identifies and locates the objects in the living room. The ultimate goal is to recognize different types of items in the living room such as electronics, furniture, carpet, and clock, and to recognize the position of the objects. Additionally, deep learning-based object detection algorithms help the user enhance the security and comfort of their automated living rooms by detecting suspicious activities. Thus the deep learning technique promotes the detection, segmentation, and classification of the objects in the images much easier. The proposed study detects and classifies the objects with the help of vortex-balsam classification more accurately. The proposed algorithm is also compared with some existing algorithms such as RNN, ResNet-50, and LSTM for enhanced results. Furthermore, the results that are obtained have shown that the proposed vortex balsam algorithm has achieved an accuracy of 98% more than any other object and proves that the proposed is the best algorithm in the object detection process.