Inspection of critical infrastructure with drones is experiencing an increasing uptake in the industry driven by a demand for reduced cost, time, and risk for inspectors. Early deployments of drone inspection services involve manual drone operations with a pilot and do not obtain the technological benefits concerning autonomy, coordination, and cooperation. In this paper, we study the design needed to handle the complexity of an Unmanned Aerial System (UAS) to support autonomous inspection of safety-critical infrastructure. We apply a constructive research approach to link innovation needs with concepts, designs, and validations that include simulation and demonstration of key design parts. Our design approach addresses the complexity of the UAS and provides a selection of technology components for drone and ground control hardware and software including algorithms for autonomous operation and interaction with cloud services. The paper presents a drone perception system with accelerated onboard computing, communication technologies of the UAS, as well as algorithms for swarm membership, formation flying, object detection, and fault detection with artificial intelligence. We find that the design of a cooperative drone swarm and its integration into a custom-built UAS for infrastructure inspection is highly feasible given the current state of the art in electronic components, software, and communication technology.

Inspection of high-voltage power lines using unmanned aerial vehicles is an emerging technological alternative to traditional methods. In the Drones4Energy project, we work toward building an autonomous vision-based beyond-visual-line-of-sight (BVLOS) power line inspection system. In this paper, we present a deep learning-based autonomous vision system to detect faults in power line components. We trained a YOLOv4-tiny architecture-based deep neural network, as it showed prominent results for detecting components with high accuracy. For running such deep learning models in a real-time environment, different single-board devices such as the Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier were used for the experimental evaluation. Our experimental results demonstrated that the proposed approach can be effective and efficient for fully automatic real-time on-board visual power line inspection.

Due to impressive performance, deep neural networks for object detection in images have become a prevalent choice. Given the complexity of the neural network models used, users of these algorithms are typically given no hint as to how the objects were found. It remains, for example, unclear whether an object is detected based on what it looks like or based on the context in which it is located. We have developed an algorithm, Surrogate Object Detection Explainer (SODEx), that can explain any object detection algorithm using any classification explainer. We evaluate SODEx qualitatively and quantitatively by detecting objects in the COCO dataset with YOLOv4 and explaining these detections with LIME. This empirical evaluation does not only demonstrate the value of explainable object detection, it also provides valuable insights into how YOLOv4 detects objects.

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FM_net” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

Designing and practical implementation of energy efficient routing protocol is always a major concern for researchers in Wireless Sensor Networks (WSNs). In the past decade various energy efficient clustering routing protocols are designed to overcome this issue. Clustered base path planning technique is widely used in both homogeneous and heterogeneous WSNs. Existing techniques are based on direct communication which consume more energy and create hot-spot near Base Station (BS). Inter-communication between nodes can play a better role in order to minimize the hot-spot issue. In this regard we propose Multi-Hop Advance Heterogeneity-aware Energy Efficient (MAHEE) clustering path planning algorithm for WSNs. In the proposed algorithm, advance Cluster Head (CH) selection algorithm is used in which nodes have higher energy will be chosen as CH. Moreover inter-cluster multi-hop routing with the help of advance CH selection can enhance the network stability and lifetime. Simulation results verify our proposed multi-hop technique give better performance over existing state of the art routing protocols.

Artificial intelligence has been widely used to enable predictive maintenance. However, AI systems need a large amount of data to generate accurate results that can be used reliably in terms of data quality. One of the ways to obtain data from the system is through the development of a digital twin. Therefore, a digital twin design might be of key value for the predictive maintenance of systems enabling the simulation of the system’s performance, anticipating potential malfunctions, and consequently reducing the cost of unforeseen failures of the physical system. In this paper, we present a framework of a digital twin system for a conveyor belt along with different sensors that collect various types of data to be analyzed by a digital system. This way, the digital twin can generate more data focusing on reducing the time to obtain enough data to train the AI algorithm properly. Furthermore, the digital twin model is designed to develop the simulation environment and integrate it with the physical system.