This conference brings together real-world practitioners and researchers in intelligent robots and computer vision to share recent applications and developments. Topics of interest include the integration of imaging sensors supporting hardware, computers, and algorithms for intelligent robots, manufacturing inspection, characterization, and/or control. The decreased cost of computational power and vision sensors has motivated the rapid proliferation of machine vision technology in a variety of industries, including aluminum, automotive, forest products, textiles, glass, steel, metal casting, aircraft, chemicals, food, fishing, agriculture, archaeological products, medical products, artistic products, etc. Other industries, such as semiconductor and electronics manufacturing, have been employing machine vision technology for several decades. Machine vision supporting handling robots is another main topic. With respect to intelligent robotics another approach is sensor fusion – combining multi-modal sensors in audio, location, image and video data for signal processing, machine learning and computer vision, and additionally other 3D capturing devices. There is a need for accurate, fast, and robust detection of objects and their position in space. Their surface, background, and illumination are uncontrolled, and in most cases the objects of interest are within a bulk of many others. For both new and existing industrial users of machine vision, there are numerous innovative methods to improve productivity, quality, and compliance with product standards. There are several broad problem areas that have received significant attention in recent years. For example, some industries are collecting enormous amounts of image data from product monitoring systems. New and efficient methods are required to extract insight and to perform process diagnostics based on this historical record. Regarding the physical scale of the measurements, microscopy techniques are nearing resolution limits in fields such as semiconductors, biology, and other nano-scale technologies. Techniques such as resolution enhancement, model-based methods, and statistical imaging may provide the means to extend these systems beyond current capabilities. Furthermore, obtaining real-time and robust measurements in-line or at-line in harsh industrial environments is a challenge for machine vision researchers, especially when the manufacturer cannot make significant changes to their facility or process.
Tire defect detection has significant industrial value and has been a research topic in both academia and industry. Despite its importance, prior works does not considered the practical manufacturing circumstances, where there are only limited annotation for the defect. Such limitation hinders the prior works from deploying to the real-world system. To address the problem of Tire Defect Detection with Limited Annotation (TTDLA), we proposed a novel framework, denoted as tire defect detection with Self-Supervision and Synthetic data (or S3). S3 first uses self-supervised learning to train the encoder without using any labeled data in the pretraining stage. The encoder is then adopted as the encoder of the Faster-RCNN detector in the fine-tuning stage. In addition, we proposed an algorithm to generate synthesized image by pasting defects randomly onto the regular image. Experiments demonstrate that both self-supervised learning and synthesized data boost the performance of the detector under TTDLA scenario.
Avoiding obstacles is challenging for autonomous robotic systems. In this work, we examine obstacle avoidance for legged hexapods, as it relates to climbing over randomly placed wooden joists. We formulate the task as a 3D joist detection problem and propose a detect-plan-act pipeline using a SLAM algorithm to generate a pointcloud and a grid map to expose high obstacles such as joists. A line detector is applied on the grid map to extract parametric information of the joist, such as height, width, orientation, and distance; based on this information the hexapod plans a sequence of leg movements to either climb over the joist or move sideways. We show that our perception and path planning module work well on the real-world joists with different heights and orientations.
In the field of automated working machines, not only is the general trend towards automation in industry, transport and logistics reflected, but new areas of application and markets are also constantly emerging. In this paper we present a pipeline for terrain classification in offroad environments and in the field of "automated maintenance of slopes", which offers potential for solving numerous socio-economic needs. Working tasks can be made more efficient, more ergonomic and, in particular, much safer, because mature, automated vehicles are used. At present, however, such tasks can only be carried out remotely or semi-automatically, under the supervision of a trained specialist. This only partially facilitates the work. The real benefit only comes when the supervising person is released from this task and is able to pursue other work. In addition to the development of a safe integrated system and sensor concept for use in public spaces as a basic prerequisite for vehicles licensed in the future, increased situational awareness of mobile systems through machine learning in order to increase their efficiency and flexibility is also of great importance.
In the past decades, developments in the field of computer vision have made both the software and hardware more capable and more easily accessible. This has enabled otherwise complex vision systems to be used in other fields, such as autonomous robotics. Although vision systems in the visible light spectrum are commonplace in robotics nowadays, the thermal spectrum is still rarely used, even though it offers certain advantages. A thermal camera can sense the temperature of objects, is independent of illumination and can actually see through heavy smoke and fog. This makes it a useful tool in particular in the field of rescue robotics, where poor vision conditions are to be expected. In this paper, the feasibility of using two thermal cameras in a stereo vision setup to map indoor scenes is to be examined. It is meant to allow an autonomous robot to perceive its indoor surroundings as a 3D space, even in poor vision conditions. The biggest challenges are the calibration of thermal cameras and the proper filtering of the raw image and the resulting disparity map. Simple and easily implemented solutions are proposed for each of these issues.
Biometric authentication takes on many forms. Some of the more researched forms are fingerprint and facial authentication. Due to the amounts of research in these areas there are benchmark datasets easily accessible for new researchers to utilize when evaluating new systems. A newer, less researched biometric method is that of lip motion authentication. These systems entail a user producing a lip motion password to authenticate, meaning they must utter the same word or phrase to gain access. Because this method is less researched, there is no large-scale dataset that can be used to compare methods as well as determine the actual levels of security that they provide. We propose an automated dataset collection pipeline that extracts a lip motion authentication dataset from collections of videos. This dataset collection pipeline will enable the collection of large-scale datasets for this problem thus advancing the capability of lip motion authentication systems.
As facial authentication systems become an increasingly advantageous technology, the subtle inaccuracy under certain subgroups grows in importance. As researchers perform data augmentation to increase subgroup accuracies, it is critical that the data augmentation approaches are understood. We specifically research the impact that the data augmentation method of racial transformation has upon the identity of the individual according to a facial authentication network. This demonstrates whether the racial transformation maintains critical aspects to an individual identity or whether the data augmentation method creates the equivalence of an entirely new individual for networks to train upon. We demonstrate our method for racial transformation based on other top research articles methods, display the embedding distance distribution of augmented faces compared with the embedding distance of non-augmented faces and explain to what extent racial transformation maintains critical aspects to an individual’s identity.