This paper presents a method for using a dual roadside seismic sensor to detect moving vehicles on roadway by installing them on a road shoulder. Seismic signals are split into fixed time intervals in recording. In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT). Various kinds of vehicle characterization...

Many recent studies have shown that deep neural models are vulnerable to adversarial samples: images with imperceptible perturbations, for example, can fool image classifiers. In this paper, we present the first type-specific approach generating examples object detection, which entails detecting bounding boxes around multiple objects in and classifying them at same time, making it a harder task...

Dynamic mechanical activity in a tunnel can be measured as ground vibrations at offset distances. These signals can be processed in sensing algorithms for detection, location, and discrimination of the activity. The objective of this work is to demonstrate that seismic simulations can reveal the effect of the environment on seismic energy as it propagates from tunnels. Using massively parallel ...

With the large-scale construction and rapid development of underground engineering, a large number of underground engineering construction, such as tunnel and subway emerged. The geological structure of the tunnel is complex and the roc is fragmented. Tunnel seismic image is a new geophysical technique. This method has advantages such as high resolution, high reliability and obvious image chara...

Seismic imaging, popularly known as tomography, borrowing the term from medical sciences, produces the raster image of the internal structure by combining information from a set of projections obtained at different viewing angles. To engineer high resolution and accurate solutions to mining problems, seismic traveltime tomography can yield a high resolution image of the subsurface to provide in...

Video object detection has been an important yet challenging topic in computer vision. Traditional methods mainly focus on designing the image-level or box-level feature propagation strategies to exploit temporal information. This paper argues that with a more effective and efficient framework, video detectors can gain improvement terms of both accuracy speed. For this purpose, studies object-l...

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common “argmax” labeling method often ignores many instances of objects. To alleviate ...