Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization

Nowadays, cost-sensitive customers need customized products that demand consumption-based production. The Internet of Things (IoT) makes ubiquitous sensing and data more available, integrating with the semantic web and advanced sensor technologies. Augmented reality (AR) is a collaborative technology that boosts user experience by coating virtual digital content into reality. Holographic communication is a transformative technology that redefines digital interaction by enabling immersive, realistic, and collaborative 3D experiences. It utilizes advanced holography to create virtual projections in real-time environments. Object detection (OD) is the most significant and challenging problem in computer vision (CV). The massive developments in deep learning (DL) models have recently considerably speeded up the OD momentum for consumer goods utilizing holographs. This article presents Object Detection with Holographic Visualization for Consumer products using a Hippopotamus Optimization Algorithm and Deep Learning (ODHVCP-HOADL) model. The aim is to develop an effective IoT-based OD system for consumer products integrated with a holographic display to provide an interactive and immersive visualization experience. Initially, the wiener filtering (WF) is utilized for image pre-processing to enhance image quality by removing unwanted noise. Furthermore, the Faster R-CNN model is employed for the OD process. The SqueezeNet model extracts and isolates relevant features from raw data. Moreover, the convolutional autoencoder (CAE) model is implemented for classification. Additionally, the hippopotamus optimization algorithm (HOA)-based hyperparameter selection model is implemented to improve the classification result of the CAE technique. Finally, the holographic process is performed by using the binary amplitude hologram (BAH) generation. The performance validation of the ODHVCP-HOADL approach is examined under the Indoor OD dataset. The comparison study of the ODHVCP-HOADL approach portrayed a superior accuracy value of 99.64% over existing models.