A Rust Removed Region Detection for Automated Rust Detection during Grinding Work Process In the maintenance task of steelmade infrastructure, such as bridges and towers, the rust removing process is one of the most dangerous and hard works for human beings. The robotic systems can provide a more efficient way to accomplish the tasks. However, the existing robotic systems cannot automatically remove the rust according to the rusted area condition during the rust removing process. As part of the solution, several rust detection methods were proposed. Although they can inspect the rust from a given image, the condition changes during the rust removing process affect the result obviously, which makes them difficult to be utilized in the real application. In our previous research, a rust detection method utilizing a sequence of digital images was proposed by observing the rusted area conditions during the rust grinding process. However, the ROI (Region of Interests) need to be manually set to estimate the change of the rusted area in the previous method. In this paper, we propose a method to determine the cleaned rusted region by an image processing technique with a low pass signal filter. The experiments were conducted on the developed rust grinding platform to show the validity of the proposed method.

A Robotic Passive Vision System for Texture Analysis in Weld Beads The use of robots is increasing in different industries, as in the case of large metal structures. The use of mobile robots meets the needs of these industries: it can be easily moved in the production line, with gains in repeatability and process efficiency, reducing rework costs. The poor configuration of the robotic and welding system generates problems in the internal structure and the surface of the weld, thus compromising the final quality of the piece. Visual inspections are common to identify problems that may have occurred during these processes. Inspections that use xrays, ultrasound, or thermal cameras require additional equipment and highly trained inspectors to analyze the results and detect problems. This work presents a computer vision system based on a passive monocular camera for analysis of weld bead textures. Images of weld beads with and without discontinuities are captured and a dimensional reduction algorithm known as Principal Component Analysis (PCA) is applied to select the main characteristics that describe each group. Afterward, the Support Vector Machine (SVM) supervised learning method is applied to recognize the patterns of image groups, making it possible to classify new images of weld beads as welds with or without discontinuities. The system makes use of the same camera coupled to the robot responsible for conducting the welding, without the need for additional sensors, and assists the welding inspector in the evaluation of the performed process.