Deep convolutional neural network-based transfer learning method for health condition identification of cable in cable-stayed bridge

The cables are extremely important and vulnerable components in the cable-stayed bridges. Because cable tension is one of the most crucial structural health indicators, therefore, assessing the cable condition based on the cable tension is a major interest in the structural health monitoring (SHM) of the cable-stayed bridges. This paper aims to develop a deep convolutional neural network (DCNN)-based transfer learning method that is integrated with a continuous wavelet transform (CWT) for the health condition identification of the cables in a cable-stayed bridge using the one-dimensional time series cable tension data. For this purpose, the CWT is adopted to convert the cable tension to the images of a time-frequency representation. The last three new layers emerged in the pre-trained DCNN model, which is called AlexNet, as a new learning framework to use for the identification of the cable condition. The performance of the proposed DCNN model is examined using several statistical measures that include accuracy, sensitivity, specificity, precision, recall, and the F-measure. The results show that the proposed DCNN model gives superior accuracy (100%) for the identification of the undamaged cables and the damaged cables based on the cable tension data

An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning

Computer vision plays a significant role in mobile robot navigation due to the abundance of information extracted from digital images. On the basis of the captured images, mobile robots determine their location and proceed to the desired destination. Obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement due to the complexity of the environment. This research provides a real-time solution to the issue of extracting corridor scenes from a single image. Using an ultra-fast semantic segmentation model to reduce the number of training parameters and the cost of computation. In addition, the mean Intersection over Union (mIoU) is 89%, and the high accuracy is 95%. To demonstrate the viability of the prosed method, the simulation results are contrasted to those of contemporary techniques. Finally, the authors employ the segmented image to construct the frontal view of the mobile robot in order to determine the available free areas for mobile robot path planning tasks