Integrating acceleration signal processing and image segmentation for condition assessment of asphalt roads

A proactive road maintenance system enables agencies to better allocate resources to manage their road networks. An inventory of the roads’ conditions is an essential component of such maintenance program. This research project proposes a hybrid system to asses the condition of the asphalt roads, which uses a dashboard-mounted smartphone to simultaneously collect the acceleration response of a vehicle and the video footage of the road surface while driving. The system analyzes acceleration data for anomalous events that could indicate a defect. Then the computer vision module of the system applies semantic segmentation in the corresponding frame to the detected anomaly to identify defects. This system demonstrated 84% recall and 88% precision rates in detection of anomalies in two road segments. Despite these promising results, the system can only detect the defects that are passed over and it could miss some defects with small acceleration responses, such as traverse cracks.

A 3D Machine Vision-Enabled Intelligent Robot Architecture

In this paper, the principle of camera imaging is studied, and the transformation model of camera calibration is analyzed. Based on Zhang Zhengyou’s camera calibration method, an automatic calibration method for monocular and binocular cameras is developed on a multichannel vision platform. The automatic calibration of camera parameters using human-machine interface of the host computer is realized. Based on the principle of binocular vision, a feasible three-dimensional positioning method for binocular target points is proposed and evaluated to provide binocular three-dimensional positioning of target in simple environment. Based on the designed multichannel vision platform, image acquisition, preprocessing, image display, monocular and binocular automatic calibration, and binocular three-dimensional positioning experiments are conducted. Moreover, the positioning error is analyzed, and the effectiveness of the binocular vision module is verified to justify the robustness of our approach.

Redes neurais convolucionais aplicas à detecção de objetos no domínio de futebol de robôs humanoides

The RoboCup Soccer is one of the largest initiatives in the robotics field of research. This initiative considers the soccer match as a challenge for the robots and aims to win a match between humans versus robots by the year of 2050. The vision module is a critical system for the robots because it needs to quickly locate and classify objects of interest for the robot in order to generate the next best action. This work evaluates deep neural networks for the detection of the ball and robots. For such task, five convolutional neural networks architectures were trained for the experiment using data augmentation and transfer learning techniques. The models were evaluated in a test set, yielding promising results in precision and frames per second. The best model achieved an mAP of 0.98 and 14.7 frames per second, running on CPU

3D VISION SYSTEM FOR UNDERWATER ROBOT

The article deals with the development of an integrated high-resolution 3D vision system for remotely controlled and autonomous underwater robotic systems. The problem of developing a 3D vision system as a sensor subsystem of an underwater robot is presented. Typical examples of tasks solved with the help of such robotic systems are environmental monitoring; detection of objects and obstacles, their localization; convergence of the CRS with the object; performing operations with objects. The structure of a complex 3D vision system is presented. The description of the design of the main structural elements of the system is given, the main functionality of the developed test software is described. The technical characteristics of the subsystems are given, such as the module for the near-vision of the 3D vision system, the module for additional lighting, and the module for far vision. The description of the developed software for processing video data of a stereo camera (stereo module) in real-time is given. The paper presents the results of the test approbation of software that implements many algorithms for 3D reconstruction of the working space of an underwater robot based on information received from the stereo vision module. The approbation was carried out on the operating model of the underwater stereo vision module connected to the developed underwater robot. The study of the characteristics of a complex 3D vision system was carried out in an aquarium. The effectiveness of the developed system was shown by the experiments with the underwater robot.

Asisten Navigasi untuk Orang dengan Gangguan Penglihatan Menggunakan Ultra Sonic (Sonar Vision) Untuk Yayasan Mitra Netra

Generally, people with visual impairments in the low vision category use sticks to find ways to deal with obstacles in front of them. However, this detection can only be felt as long as the stick is used. The purpose of this community service activity is to made a sonar vision set using a sonar sensor that can be attached to the wrist and belt. This device is designed to detect obstacles up to a distance of 3 meters. This activity was carried out at the Mitra Netra Foundation. The approach used is the creation, provision and education of the use of the Sonar Vision module for people with visual impairments. The results of this activity show that 100 low vision sufferers have benefited from this system, where 84% of them feel that sonar vision helps them in navigation.

Practical application of a safe human-robot interaction software

Purpose Because of the increased use of robots in the industry, it has become inevitable for humans and robots to be able to work together. Therefore, human security has become the primary noncompromising factor of joint human and robot operations. For this reason, the purpose of this study was to develop a safe human-robot interaction software based on vision and touch. Design/methodology/approach The software consists of three modules. Firstly, the vision module has two tasks: to determine whether there is a human presence and to measure the distance between the robot and the human within the robot’s working space using convolutional neural networks (CNNs) and depth sensors. Secondly, the touch detection module perceives whether or not a human physically touches the robot within the same work environment using robot axis torques, wavelet packet decomposition algorithm and CNN. Lastly, the robot’s operating speed is adjusted according to hazard levels came from vision and touch module using the robot’s control module. Findings The developed software was tested with an industrial robot manipulator and successful results were obtained with minimal error. Practical implications The success of the developed algorithm was demonstrated in the current study and the algorithm can be used in other industrial robots for safety. Originality/value In this study, a new and practical safety algorithm is proposed and the health of people working with industrial robots is guaranteed.