Shallow buried improvised explosive device detection via convolutional neural networks

Abstract

The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method.

An integrated low-cost system for object detection in underwater environments

“…Sea Turtle 12 : this category of animals can be exchanged for grenades or other kinds of explosive materials, due to their carapace; 4. Pufferfish 13 : this kind of fish may resemble to a bomb in certain circumstances (especially when it inflates its body); 5. Torpedo 14 : another kind of animal which can be exchanged for a bomb (especially when it plunges into the sand of the seabed); 6.…”

Buried Wire Detection and Clustering Algorithm with Ground Penetrating Radar

“…SVD-based image enhancement is well studied in the literature. It is performed by decomposing B-scan image X (with dimensions M x N) into different spectral components using SVD, i.e., (1) where, U and V have dimensions M x M, N x N are unitary matrices, and S = diag(s1, s2, …., sM) with s1 ≥ s2 ≥….≥ sM ≥ 0 are singular values of X [11].…”

“…The problem of detecting improvised explosive devices is a critical concern for governments in the developing world [1]. Irregularity of shape and contents in the structure of IEDs makes them quite hard to detect with most sensors [2].…”

Image-based textile decoding

“…Due to industrial demand, the pattern analysis by DNNs has been introduced for contentbased image retrieval [10], noisy image recognition [11], 3D medical image super-resolution [12], video surveillance [13], foreground detection [14], multi-object tracking [15], explosive device detection [16], pupil detection [17], online data streaming [18], airport baggage handling [19], and many other objectives. Computational photography [20] and purpose-specific machine learning [21] have been also employed to solve industrial problems.…”