Detecting Submerged Bodies: Controlled Research Using Side‐Scan Sonar to Detect Submerged Proxy Cadavers

Healy, Carrie; Schultz, John J.; Parker, Andrew; Lowers, Bim

doi:10.1111/1556-4029.12671

Cited by 16 publications

(12 citation statements)

References 25 publications

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“…If remains are submerged, particularly in stagnant water, it may be necessary for the water to be agitated to aid the release of decomposition VOCs from the water. This may affect Victim Recovery Dogs' ability to pin-point the exact location of remains, although other instrumentation, such as side-scan sonar, may address this problem [61]. Furthermore, the solubility of some VOCs within the water may help to explain the phenomenon of dogs "tasting" the water by lapping at the water prior to giving an alert, which has been observed on numerous occasions with different dogs.…”

Section: Water Typementioning

confidence: 99%

Identification of decomposition volatile organic compounds from surface-deposited and submerged porcine remains

et al. 2019

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Cadaver dogs are routinely used internationally by police and civilian search organisations to locate human remains on land and in water, yet little is currently known about the volatile organic compounds (VOCs) that are released by a cadaver underwater; how this compares to those given off by a cadaver deposited on land; and ultimately, how this affects the detection of drowned victims by dogs. The aim of this study was to identify the VOCs released by whole porcine (Sus scrofa domesticus) cadavers deposited on the surface and submerged in water using solid phase microextraction gas chromatography mass spectrometry (SPME GC-MS) to ascertain if there are notable differences in decomposition odour depending on the deposition location. For the first time in the UK, the volatile organic compounds (VOCs) from the headspace of decomposing porcine cadavers deposited in both terrestrial and water environments have been detected and identified using SPME-GCMS, including thirteen new VOCs not previously detected from porcine cadavers. Distinct differences were found between the VOCs emitted by porcine cadavers in terrestrial and water environments. In total, seventy-four VOCs were identified from a variety of different chemical classes; carboxylic acids, alcohols, aromatics, aldehydes, ketones, hydrocarbons, esters, ethers, nitrogen compounds and sulphur compounds. Only forty-one VOCs were detected in the headspace of the submerged pigs with seventy detected in the headspace of the surface-deposited pigs. These deposition-dependent differences have important implications for the training of cadaver dogs in the UK. If dog training does not account for these depositional differences, there is potential for human remains to be missed. Whilst the specific odours that elicit a trained response from cadaver dogs remain unknown, this research means that recommendations can be made for the training of cadaver dogs to incorporate different depositions, to account for odour differences and mitigate the possibility of missed human remains operationally. 1.1. VOCs from the decomposition of carbohydrates Carbohydrates are broken down into their constituent sugars and

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Section: Water Typementioning

confidence: 99%

Identification of decomposition volatile organic compounds from surface-deposited and submerged porcine remains

et al. 2019

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“…Step 5) The gradient amplitude and direction angle are calculated using Equations (3) and (4). The horizontal and vertical gradient amplitudes are calculated using Sobel's first-order differential template.…”

Section: Fusion Methods Experiments Processmentioning

confidence: 99%

“…The method has been widely used due to its high precision, high efficiency, and high seafloor image quality. SSS is commonly used in marine surveying and mapping, submarine geological exploration, and detection of seabed features [1][2][3][4][5]. In the generated grayscale image, the sea bottom lines, which are the obvious boundaries between the water column and image areas, are composed of a series of submarine points, which is the first real seafloor echo point along the track direction of each ping datum [6].…”

Section: Introductionmentioning

confidence: 99%

Bottom Tracking Method Based on LOG/Canny and the Threshold Method for Side-scan Sonar

Wang¹,

Li²,

Li³

et al. 2019

JESTR

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The detection of sea bottom lines is affected by the noise in the water column between the towfish and the seabed. Under these circumstances, using one edge detection technology to detect the bottom line accurately is difficult. A fusion method based on LOG/Canny operator and the threshold approach was proposed in this study to determine sea bottom lines in sonar images accurately. With this method, images filtered by the LOG function and sharpened by a convolution template were utilized as the input images of the Canny detection algorithm to implement bottom tracking. The tracking result was fused with that of the threshold method to obtain merged submarine lines. Interpolation and filtration were subsequently implemented to eliminate outliers. The result was then used to accurately detect sea bottom lines. The measured height and depth were compared using a single-beam depthometer. Results demonstrate that the bottom line is difficult to accurately detect by using only edge detection technologies, such as LOG and Canny, or the threshold method under complex situations in the water column. By contrast, the proposed fusion method can perform detection accurately. Take Mirs Bay of the Shenzhen city as an example, the mean square error of tracking accuracy is determined to be ±0.192 m, which verifies the feasibility of the proposed method. This study provides a reference for the accurate detection of submarine lines in complex conditions wherein large amounts of noise exist in the water column.

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“…Side-scan sonar (SSS) is often applied to ocean engineering for obtaining high-resolution seabed images and detecting underwater targets such as aircrafts, shipwrecks, torpedoes, mine-like objects, etc. [1][2][3][4][5][6]. In practice, target detection and recognition from SSS images are mostly based on manual interpretation, and thus its efficiency and accuracy are significantly affected by the experience of the sonar map reader.…”

Section: Introductionmentioning

confidence: 99%

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

et al. 2019

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In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model.

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Detecting Submerged Bodies: Controlled Research Using Side‐Scan Sonar to Detect Submerged Proxy Cadavers

Cited by 16 publications

References 25 publications

Identification of decomposition volatile organic compounds from surface-deposited and submerged porcine remains

Identification of decomposition volatile organic compounds from surface-deposited and submerged porcine remains

Bottom Tracking Method Based on LOG/Canny and the Threshold Method for Side-scan Sonar

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

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