I See a Car Crash: Real-Time Detection of Small Scale Incidents in Microblogs

Author(s):  
Axel Schulz ◽  
Petar Ristoski ◽  
Heiko Paulheim
2012 ◽  
Vol 562-564 ◽  
pp. 1972-1976
Author(s):  
Jun Wu ◽  
Shen Qi Ding ◽  
Kui Yu ◽  
Xiao Biao Li

For ensuring the safety of inland river shipping and avoiding super draft, it is of significance to propose a method to detect draft of inland departure ship. The real-time detection method on draft of inland departure ship is proposed, the measurement principle of ship draft is deeply analyzed, and the small scale experiment has been carried out. Experimental results show that as ship speed inicreases, the ship draft is of gradually declining tendency, and the measurement error is of gradually increasing tendency, the minium error is -4.2%, the maximum error is -11.4%. The results prove the possibility of this method , and also indicate that ship speed has large influence on measured ship draft.


2021 ◽  
Author(s):  
Bixen Telletxea ◽  
Mar Tapia ◽  
Marta Guinau ◽  
Manuel J. Royán ◽  
Pere Roig Lafon ◽  
...  

<p>Seismic sensors installed in areas prone to rockfalls provide a continuous record of the phenomenon, allowing real-time detection and characterization. Detection of small scale rockfalls (< 0.001 m<sup>3</sup>), that might be precursors of larger events, can be worthwhile for early warning systems of rockfalls. However, seismic signals are closely dependent on the characteristics of the event and on the geotechnical characteristics of the ground, making the detection of small rockfalls complex and requiring detailed in-situ analyzes. For this reason, an experiment was carried out on the UB experimental site (Puigcercós Cliff, Catalonia, NE Spain) on 6<sup>th</sup>-7<sup>th</sup> of June 2013, where 21 rocks with volumes ranging from 0.0015 m<sup>3</sup> to 0.0004 m<sup>3</sup> were thrown from the top of the cliff (200 m long and 27 m high) and the seismic signals were registered with three 3D short period seismic sensors located at different distances from the rock wall: 57 m, 67 m, and 107 m.</p><p>The recorded seismic signals have a frequency content between 10-30 Hz, and the duration of the peak amplitudes varied between 0.3 and 0.6 s. Based on these characteristics, different phases of the dynamics of the rockfalls were identified, including main impacts, rebounds, flights, rolling and final stop of the events. The furthest station recorded the lowest frequency and amplitude values, limiting our ability to detect those blocks smaller than 0.0015 m<sup>3</sup>. Comparing the results with the nearest station, seismic attenuation phenomena is detectable even at distances of 50 m.</p><p>After the experiment, a permanent seismic station was installed in the area, at 107 m from the cliff. Using LiDAR and 2D imagery monitoring, two naturally triggered rockfalls were identified on 30<sup>th</sup> and 31<sup>st</sup> August 2017 (0.28 m<sup>3</sup> and 0.25 m<sup>3</sup> respectively). Based on the results from the experiment and an automatic detection system, these main events and prior minor events have been found in the continuous seismic records of this permanent station. The characteristics of these natural detachments differ partially from the artificially triggered rockfalls during the experiment, since the geometry of the seismic signals is different. The observed shapes of the natural detachments are similar to that of granular flows, much more continuous than the sharp shapes that were observed in the isolated blocks of the experiment. This shows the possibility of incorporating seismic stations for the automatic detection and initial characterization of rockfalls and its effectiveness in detecting frequencies of occurrence.</p><p>In order to evaluate the possibility of estimating rockfall volumes, diverse energy ratios (<em>E<sub>s</sub>/E<sub>p</sub></em>) were calculated. However, precise volume estimation is not possible. Nevertheless, the combination of seismic data with LiDAR and photographic techniques allows accurate new volume calculations of rockfalls to be incorporated progressively into the study of rockfalls.</p><p>ACKNOWLEDGMENTS: The authors would like to acknowledge the financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, Spanish MINEICO. We are also thankful to Origens UNESCO Global Geopark.</p>


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8146
Author(s):  
Haozhen Zhu ◽  
Yao Xie ◽  
Huihui Huang ◽  
Chen Jing ◽  
Yingjiao Rong ◽  
...  

With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships in SAR images requires more powerful multi-scale detectors. To address these issues, a SAR ship detector called Duplicate Bilateral YOLO (DB-YOLO) is proposed in this paper, which is composed of a Feature Extraction Network (FEN), Duplicate Bilateral Feature Pyramid Network (DB-FPN) and Detection Network (DN). Firstly, a single-stage network is used to meet the need of real-time detection, and the cross stage partial (CSP) block is used to reduce the redundant parameters. Secondly, DB-FPN is designed to enhance the fusion of semantic and spatial information. In view of the ships in SAR image are mainly distributed with small-scale targets, the distribution of parameters and computation values between FEN and DB-FPN in different feature layers is redistributed to solve the multi-scale detection. Finally, the bounding boxes and confidence scores are given through the detection head of YOLO. In order to evaluate the effectiveness and robustness of DB-YOLO, comparative experiments with the other six state-of-the-art methods (Faster R-CNN, Cascade R-CNN, Libra R-CNN, FCOS, CenterNet and YOLOv5s) on two SAR ship datasets, i.e., SSDD and HRSID, are performed. The experimental results show that the AP50 of DB-YOLO reaches 97.8% on SSDD and 94.4% on HRSID, respectively. DB-YOLO meets the requirement of real-time detection (48.1 FPS) and is superior to other methods in the experiments.


2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown

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