Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.

Object Detection in 20 Years: A Survey

In this paper, we provide a survey of the traffic sign detection literature, detailing detection systems for traffic sign recognition (TSR) for driver assistance. We separately describe the contributions of recent works to the various stages inherent in traffic sign detection: segmentation, feature extraction, and final sign detection. While TSR is a well-established research area, we highlight open research issues in the literature, including a dearth of use of publicly available image databases and the over-representation of European traffic signs. Furthermore, we discuss future directions of TSR research, including the integration of context and localization. We also introduce a new public database containing U.S. traffic signs.

/pdf/vision-based-traffic-sign-detection-and-analysis-for-5mamysz2ii.pdf

Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey

This paper proposes a novel system for the automatic detection and recognition of traffic signs. The proposed system detects candidate regions as maximally stable extremal regions (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on a cascade of support vector machine (SVM) classifiers that were trained using histogram of oriented gradient (HOG) features. The training data are generated from synthetic template images that are freely available from an online database; thus, real footage road signs are not required as training data. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 20 frames per second, and recognizes all classes of ideogram-based (nontext) traffic symbols from an online road sign database. Comprehensive comparative results to illustrate the performance of the system are presented.

Real-Time Detection and Recognition of Road Traffic Signs

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.

/pdf/overview-of-environment-perception-for-intelligent-vehicles-7s8l9oth23.pdf

Overview of Environment Perception for Intelligent Vehicles

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

We propose new ideas to directly search for light dark matter, such as the axion or the dark photon, by using magnetometry with nitrogen-vacancy centers in diamonds. If the dark matter couples to the electron spin, it affects the evolution of the Bloch vectors consisting of the spin triplet states, which may be detected through several magnetometry techniques. We give several concrete examples with the use of dc and ac magnetometry and estimate the sensitivity on dark matter couplings.

pdf/light-dark-matter-search-with-nitrogen-vacancy-centers-in-2pwv6gq0.pdf

Light Dark Matter Search with Nitrogen-Vacancy Centers in Diamonds

Detonation nanodiamond (DND) is the smallest class of diamond nanocrystal capable of hosting various color centers with a size akin to molecular pores. Their negatively charged nitrogen-vacancy center (NV−) is a versatile tool for sensing a wide range of physical and even chemical parameters at the nanoscale. The NV− is, therefore, attracting interest as the smallest quantum sensor in biological research. Nonetheless, recent NV− enhancement in DND has yet to yield sufficient fluorescence per particle, leading to efforts to incorporate other group-IV color centers into DND. An example is adding a silicon dopant to the explosive mixture to create negatively charged silicon-vacancy centers (SiV−). In this paper, we report on efficient observation (∼50% of randomly selected spots) of the characteristic optically detected magnetic resonance (ODMR) NV− signal in silicon-doped DND (Si-DND) subjected to boiling acid surface cleaning. The NV− concentration is estimated by continuous-wave electron spin resonance spectroscopy to be 0.35 ppm without the NV− enrichment process. A temperature sensitivity of 0.36K/Hz in an NV− ensemble inside an aggregate of Si-DND is achieved via the ODMR-based technique. Transmission electron microscopy survey reveals that the Si-DNDs core sizes are ∼11.2 nm, the smallest among the nanodiamond’s temperature sensitivity studies. Furthermore, temperature sensing using both SiV− (all-optical technique) and NV− (ODMR-based technique) in the same confocal volume is demonstrated, showing Si-DND’s multimodal temperature sensing capability. The results of the study thereby pave a path for multi-color and multimodal biosensors and for decoupling the detected electrical field and temperature effects on the NV− center.

Small multimodal thermometry with detonation-created multi-color centers in detonation nanodiamond

We study traffic sign detection on a challenging large-scale real-world dataset of panoramic images. The core processing is based on the Histogram of Oriented Gradients (HOG) algorithm which is extended by incorporating color information in the feature vector. The choice of the color space has a large influence on the performance, where we have found that the CIELab and YCbCr color spaces give the best results. The use of color significantly improves the detection performance. We compare the performance of a specific and HOG algorithm, and show that HOG outperforms the specific algorithm by up to tens of percents in most cases. In addition, we propose a new iterative SVM training paradigm to deal with the large variation in background appearance. This reduces memory consumption and increases utilization of background information.

/pdf/color-exploitation-in-hog-based-traffic-sign-detection-4vjg8uf9qv.pdf

Color exploitation in hog-based traffic sign detection

Nuclear spin metrology with nitrogen vacancy center in diamond for axion dark matter detection

Exquisite sensitivities are a prominent advantage of quantum sensors. Ramsey sequences allow precise measurement of direct current ﬁelds, while Hahn-echo-like sequences measure alternating current ﬁelds. However, the latter are restrained for use with high-frequency ﬁelds (above approximately 1 kHz) due to ﬁnite coherence times, leaving less-sensitive noncoherent methods for the low-frequency range. In this paper, we propose to bridge the gap with a ﬁtting-based algorithm with a frequency-independent sensitivity to coherently measure low-frequency ﬁelds. As the algorithm beneﬁts from coherence-based measurements, its demonstration with a single nitrogen-vacancy center gives a sensitivity of 9 . 4 nT Hz − 0 . 5 for frequencies below about 0 . 6 kHz down to near-constant ﬁelds. To inspect the potential in various scenarios, we apply the algorithm at a background ﬁeld of tens of nTs, and we measure low-frequency signals via synchronization.

Low-Frequency Quantum Sensing

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