Isaac Osei Agyemang

Isaac Osei Agyemang, Isaac Adjei-Mensah, Daniel Acheampong et al.

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of infrastructure, but complex image environments, noisy labels, and reliance on manual damage assessments often hinder its effectiveness. This study introduces the Guided Detection Network (Guided-DetNet), a framework designed to address these challenges. Guided-DetNet is characterized by a Generative Attention Module (GAM), Hierarchical Elimination Algorithm (HEA), and Volumetric Contour Visual Assessment (VCVA). GAM leverages cross-horizontal and cross-vertical patch merging and cross-foreground-background feature fusion to generate varied features to mitigate complex image environments. HEA addresses noisy labeling using hierarchical relationships among classes to refine instances given an image by eliminating unlikely class instances. VCVA assesses the severity of detected damages via volumetric representation and quantification leveraging the Dirac delta distribution. A comprehensive quantitative study and two robustness tests were conducted using the PEER Hub dataset, and a drone-based application, which involved a field experiment, was conducted to substantiate Guided-DetNet's promising performances. In triple classification tasks, the framework achieved 96% accuracy, surpassing state-of-the-art classifiers by up to 3%. In dual detection tasks, it outperformed competitive detectors with a precision of 94% and a mean average precision (mAP) of 79% while maintaining a frame rate of 57.04fps, suitable for real-time applications. Additionally, robustness tests demonstrated resilience under adverse conditions, with precision scores ranging from 79% to 91%. Guided-DetNet is established as a robust and efficient framework for SHM, offering advancements in automation and precision, with the potential for widespread application in drone-based infrastructure inspections.

Isaac Osei Agyemang, Isaac Adjei Mensah, Sophyani Banaamwini Yussif et al.

Micro-drones can be integrated into various industrial applications but are constrained by their computing power and expert pilots, a secondary challenge. This study presents a computationally-efficient deep convolutional neural network that utilizes Gabor filters and spatial separable convolutions with low computational complexities. An attention module is integrated with the model to complement the performance. Further, perception-based action space and trajectory generators are integrated with the model's predictions for intuitive navigation. The computationally-efficient model aids a human operator in controlling a micro-drone via gestures. Nearly 18% of computational resources are conserved using the NVIDIA GPU profiler during training. Using a low-cost DJI Tello drone for experiment verification, the computationally-efficient model shows promising results compared to a state-of-the-art and conventional computer vision-based technique.

Isaac Adjei-Mensah, Isaac Osei Agyemang, Collins Sey et al.

From online education and trading, all aspects of our lives are affected by digital technology. Among them, the storage of music has also entered the digital era. Music productions created by artists have brought great joy to people. However, when artists upload their works, they are most downloaded and reprinted by others, and copyright information and the issue associated with the sharing of music arise. This will have a significant negative impact on the enthusiasm and motivation of artists. This paper provides an internet database platform for artists, which uses the distributed and tamper-proof technology of the Ethereum blockchain to store music works, and protect the copyright information of each album or music produced by artists in the music industry. Design and implementation of the system model and data storage are proposed and data storage processes based on the Ethereum smart contract are demonstrated in detail. The system stores music information on the blockchain network, using the smart contract to provide artists with a fast and efficient royalty payment. Node.js is applied to carry out the experiments of our system, and we test Remote Procedure Calls (RPC) with available account and private keys for contract development and use block explorer to track music information on the blockchain. Our system enables copyright revenue to be attributed to music creators that will help to eliminate the illegal uploading of music on other websites.