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Objects in unmanned aerial vehicle (UAV) images are generally small due to the high-photography altitude. Although many efforts have been made in object detection, how to accurately and quickly detect small objects is still one of the remaining open challenges. In this paper, we propose a feature fusion and scaling-based single shot detector (FS-SSD) for small object detection in the UAV images. The FS-SSD is an enhancement based on FSSD, a variety of the original single shot multibox detector (SSD). We add an extra scaling branch of the deconvolution module with an average pooling operation to form a feature pyramid. The original feature fusion branch is adjusted to be better suited to the small object detection task. The two feature pyramids generated by the deconvolution module and feature fusion module are utilized to make predictions together. In addition to the deep features learned by the FS-SSD, to further improve the detection accuracy, spatial context analysis is proposed to incorporate the object spatial relationships into object redetection. The interclass and intraclass distances between different object instances are computed as a spatial context, which proves effective for multiclass small object detection. Six experiments are conducted on the PASCAL VOC dataset and the two UAV image datasets. The experimental results demonstrate that the proposed method can achieve a comparable detection speed but an accuracy superior to those of the six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In view of the great diversity and complexity of social images, it is of great significance to improve the performance of personalized recommendation by learning a user interest from large-scale social images. Deep learning, as the latest research in the field of artificial intelligence, provides a new personalized recommendation solution of social images for learning a user's interest. Moreover, social image sharing websites (such as Flickr) allow users to tag uploaded images with tags. As an important image semantic cue, effective tags not only represent the latent image information but also show personalized user interest. Therefore, a personalized recommendation method of social image is proposed by constructing a user-interest tree with deep features and tag trees in this paper. The main contributions of our paper are as follows: first, to efficiently make use of tags, a tag tree of social images is created by the re-ranked tags; second, for compactly representing the image content, deep features are learned by training the AlexNet network; third, a user-interest tree is constructed with deep features and tag trees that include the user-interest tree of social images and the user-interest tree of tags, respectively, and finally, a personalized recommendation system of social images is built based on a user-interest tree. Experiments on the NUS-WIDE dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized recommendations. [ABSTRACT FROM AUTHOR]

Our series of profiles about entrepreneurs in IEEE Engineering Management Review features movers and shakers in the technology industries. Entrepreneurship requires leaders who are innovative, proactive, and risk-takers – people who embrace the technology as well as the business of start-ups. In these articles, we provide a look at the innovative thought processes and practices of these entrepreneurs – what got them where they are today. We also share lessons learned as the entrepreneurs created and grew their ventures. Many of these entrepreneurs will be attending Technology and Engineering Management Society events over the next couple years. Take the opportunity to meet them as you can, especially if you are thinking about launching your own new venture. Our featured entrepreneur for this article is Yael Schwartz. [ABSTRACT FROM AUTHOR]

We continue the IEEE Engineering Management Review Technology Manager's Notebook series of tech entrepreneur profiles, the movers and shakers in technology industries. Entrepreneurship requires change agents, leaders who are innovative, proactive, and risk-takers. Entrepreneurship often requires technology and engineering insights. In these articles, we provide a look at the innovative thought processes and practices of these entrepreneurs—what got them where they are today. We also tap into their experiences in order to share lessons learned with the broader community. Many of these entrepreneurs will be attending Technology and Engineering Management Society events over the next couple years. Take the opportunity to meet them as you can, especially if you are thinking about launching your own new venture. Our featured entrepreneur for this article is Ellen Christopherson. [ABSTRACT FROM AUTHOR]

A compact solid-state UV flame sensing system is developed and tested. We have addressed two approaches for obtaining additional six orders of long-wavelength rejection power on top of that of the built-in UV sensor based on a wide-bandgap II–VI Schottky-barrier structure. An op-amp-based amplification circuit with an average gain of 16 600 is used for signal amplification. It is demonstrated that the developed sensing system could detect a standard butane-air flame with excellent solar-blind characteristics, potentially capable for monitoring industrial boilers. Several feasible approaches were discussed for further improvement in the performance of the developed sensing system targeting its general usage in fire-safety applications. [ABSTRACT FROM PUBLISHER]

Roads, as important artificial objects, are the main body of modern traffic system, providing many conveniences for human civilization. With the development of Intelligent Transportation Systems (ITS), the road structure is changing frequently. Road recognition is to identify the road type from remote sensing imagery, and road types depend largely on the characteristics of roads. Thus, how to extract road features and further making road classification efficient have become a popular and challenging research topic. In this paper, we propose a road recognition method for remote sensing imagery using incremental learning. In principle, our method includes the following steps: 1) the non-road remote sensing imagery is first filtered by using support vector machine; 2) the road network is obtained from the road remote sensing imagery by computing multiple saliency features; 3) the road features are extracted from road network and background environment; and 4) the roads are recognized as three road types according to the classification results of incremental learning algorithm. The experimental results show that our method has higher road recognition rate as well as less recognition time than the other popular algorithms. [ABSTRACT FROM PUBLISHER]

Over the next few issues of IEEE Engineering Management Review, we will feature profiles of some of the many entrepreneurs who are movers and shakers in a variety of fields as part of a technology manager's notebook series. Entrepreneurship requires change agents, leaders who are innovative, proactive, and risk-takers.1 Entrepreneurship also often requires technology and engineering insights. In these articles, we will provide a look at the innovative thought processes and practices of these entrepreneurs—what got them where they are today. We will also tap into their experiences in order to share lessons learned with the broader community. Many of these entrepreneurs will be attending Technology and Engineering Management Society events over the next couple years. Take the opportunity to meet them as you can, especially if you are thinking about launching your own new venture. Our first featured entrepreneur is Maria Rios. [ABSTRACT FROM AUTHOR]