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.

Deep Learning Based Automatic Crack Detection and Segmentation for Unmanned Aerial Vehicle Inspections Automatic crack detection and segmentation plays a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and the performance of it is compared with other state of the art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmarkdataset on the internet for crack detection and segmentation, which is opensourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmarkdataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed for the crack inspection tasks of various concrete structures.