Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Salient Object Detection: A Benchmark

Abstract: We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted just two years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for state-of-the-art models, provide useful hints towards constructing more challenging large scale datasets and better saliency models. Finally, we propose probable solutions for tackling several open problems such as evaluation scores and dataset bias, which also suggest future research directions in the rapidly-growing field of salient object detection.

A fast and robust convolutional neural network-based defect detection model in product quality control

Abstract: The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods.

A high thermoelectric figure of merit ZT > 1 in Ba heavily doped BiCuSeO oxyselenides

Abstract: A high ZT value of ∼1.1 at 923 K in the BiCuSeO system is achieved via heavily doping with Ba and refining grain sizes (200–400 nm), which is higher than any thermoelectric oxide. Excellent thermal and chemical stabilities up to 923 K and high thermoelectric performance confirm that the BiCuSeO system is promising for thermoelectric power generation applications.

Effect of arc mode in cold metal transfer process on porosity of additively manufactured Al-6.3%Cu alloy

Abstract: In this study, the effect of arc mode in cold metal transfer (CMT) process on the porosity characteristic of additively manufactured Al-6.3%Cu alloy has been systematically investigated. The variants include conventional CMT, CMT pulse (CMT-P), CMT advanced (CMT-ADV) and CMT pulse advanced (CMT-PADV) and experiments were performed on both single layer deposits and multilayer deposits. The mechanism of porosity generation using the CMT arc mode variants is discussed. It was found that deposit porosity is significantly influenced by the arc mode type of CMT process. Conventional CMT is not suitable for the additive manufacturing process because it produces a large amount of gas pores, even in single layer deposit. CMT-PADV proved to be the most suitable process for depositing aluminium alloy due to its excellent performance in controlling porosity. With correct parameter, setting the gas pores can be eliminated. It was found that the key factors that enable the CMT-PADV process to control the porosity efficiently are the low heat input, a fine equiaxed grain structure and effective oxide cleaning of the wire.