Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies% at the frame level, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., ``vandalism'', is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks, UCFCrime-AR and XDViolence-AR, constructed on top of prevalent anomaly datasets. Meanwhile, we design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Lightning Damage Testing of Aircraft Composite-Reinforced Panels and Its Metal Protection Structures

In order to investigate the lightning damage behavior of an aircraft carbon fiber/epoxy composite-reinforced panel and its protection structures, four types of panels were selected to carry out a lightning experiment. Panels were without protection, with a full-sprayed aluminum coating, a local-sprayed aluminum coating, and a full-embedded copper mesh filling, respectively. Their surface and internal damage was detected via ultrasonic C-scanning. Results showed delamination damage for the protected and unprotected specimens due to substantial lightning Joule heat, thermal shock, and internal explosion. The aluminum coatings and the copper mesh had good shielding performance against anti-lightning strike damage. The protection method with a full-sprayed aluminum coating is more effective compared with the other two methods. This study is valuable to investigate the protection effectiveness of metal covers when aircraft composite structures are struck by lightning

3D S-wave velocity structure of the Ningdu basin in Jiangxi province inferred from ambient noise tomography with dense array

The Ningdu basin, located in southern Jiangxi province of southwest China, is one of the Mesozoic basin groups which has exploration prospects for geothermal energy. A study on the detailed velocity structure of the Ningdu basin can provide important information for geothermal resource exploration. In this study, we deployed a dense seismic array in the Ningdu basin to investigate the 3D velocity structure and discuss implications for geothermal exploration and geological evolution. Based on the dense seismic array including 35 short-period (5 s-100 ​Hz) seismometers with an average interstation distance of ∼5 ​km, Rayleigh surface wave dispersion curves were extracted from the continuous ambient noise data for surface wave tomographic inversion. Group velocity tomography was conducted and the 3D S-wave velocity structure was inverted by the neighborhood algorithm. The results revealed obvious low-velocity anomalies in the center of the basin, consistent with the low-velocity Cretaceous sedimentary rocks. The basement and basin-controlling fault can also be depicted by the S-wave velocity anomalies. The obvious seismic interface is about 2 ​km depth in the basin center and decreases to 700 ​m depth near the basin boundary, suggesting spatial thickness variations of the Cretaceous sediment. The fault features of the S-wave velocity profile coincide with the geological cognition of the western boundary basin-controlling fault, which may provide possible upwelling channels for geothermal fluid. This study suggests that seismic tomography with a dense array is an effective method and can play an important role in the detailed investigations of sedimentary basins

Inversion of Rayleigh Wave Dispersion Curves via Long Short-Term Memory Combined with Particle Swarm Optimization

An essential step in surface wave exploration is the inversion of dispersion curves. By inverting dispersion curves, we can effectively establish the shear-wave velocity model and obtain reliable subsurface stratigraphic information. The inversion of dispersion curves is an inversion problem with multiple parameters and multiple poles, and obtaining a high precision solution is difficult. Among the methods of inversion of dispersion curves, local search methods are prone to fall into local extremes, and global search methods such as particle swarm optimization (PSO) and genetic algorithm (GA) present the disadvantages of slow convergence speed and low precision. Deep learning models with strong nonlinear mapping capability can effectively solve nonlinear problems. Therefore, we propose a method called PSO-optimized long short-term memory (LSTM) network (PSO-LSTM) to invert the dispersion curves in order to improve the effect of inversion of dispersion curves. The method is based on the LSTM network, and PSO is used to optimize the LSTM network structure and other parameters that need to be given manually to improve the prediction of the network. Two theoretical geological models are used in the paper: Model A and Model B to test the PSO-LSTM. The tests include the noisy data test and noise-free data test. Model A was tested without noise, and Model B was tested with noise. In addition, PSO and LSTM were tested on model A to compare the performance of PSO-LSTM. In Model A, the maximum relative errors of PSO and LSTM are 20.76% and 5.85%, respectively, and the maximum standard deviations of PSO and LSTM are 57.37 and 1.97, respectively. For PSO-LSTM, the maximum relative errors of Model A and Model B in the inverse results are 2.05% and 2.09%, and the maximum standard deviations of Model A and Model B in the inverse results are 1.23 and 3.87, respectively. The test results of Model A show that the inversion performance of PSO-LSTM is better than those of LSTM and PSO, and the performance of the network can be improved after PSO is used to optimize the network parameters. The inverse results from Model B show that the PSO-LSTM is robust and can invert the dispersion curves well even after adding noise to the model. Finally, the PSO-LSTM is used to invert the actual data from Wyoming, USA, which demonstrates that the PSO-LSTM can be used for the quantitative interpretation of Rayleigh wave dispersion curves