A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

Human Diver-Inspired Visual Navigation: Towards Coverage Path Planning of Shipwrecks

This work proposes vision-only navigation strategies for an autonomous underwater robot. This approach is a step towards solving the coverage path planning problem in a 3-D environment for surveying underwater structures. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work, we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this article is a step towards enabling the autonomous operation of lightweight robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3-D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation with 80% accuracy on the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed.

Underwater Technical Inspections Using ROV Applied to Maritime and Coastal Engineering: The Study Case of Canary Islands

Underwater Technical Inspections using ROV have an important role in the design, construction, maintenance and repair of maritime and coastal infrastructures, trough video recording, digital photographs, collection of technical data and underwater topographic survey providing support for consultancy studies and projects and technical advice and appraisals. Routine inspections are the key to the maintenance of any submerged infrastructure. The importance of this type of inspection is increasing every day, but divers are also placed in increasingly dangerous scenarios to carry out this type of work. Inspections of underwater structures (as in dams, bridges, reservoirs, breakwaters, piers, oil rigs, etc.) have always been arduous and difficult, and often dangerous, but today underwater drones offer solutions that eliminate the risk faced by divers, and that also greatly reduce the high costs involved in such inspections.