Framework for Structural Health Monitoring of Steel Bridges by Computer Vision The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic wellbeing of society. At the same time, due to the continuous development, rising wealth of the society and socioeconomic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easytouse and relatively lowcost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial VehicleBased Remote Sensing and Digital Image Processing. In our study, we present a stateoftheart framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.

Bend Detection of Bridge Chords in UAV Images via RegionBased Deep Semantic Segmentation Network Visionbased techniques are gradually being applied to civil engineering fields by aiding human resources to inspect and assess the condition of structures. This paper presents a visionbased inspection system to detect bends of the bridge chords in UAV images. The main parts of this paper firstly present a novel region proposal algorithm to localize bridge chord bends without distinct boundaries and then present a fusionbased deep semantic segmentation network that improves the detection performance by fusing spatial information from neighborhood. The proposed system achieves approximate 0.7762 Mean IOU and 0.6424 BF Score on the field dataset, which outperforms the other stateoftheart systems.