{"id":4396,"date":"2019-03-25T17:43:43","date_gmt":"2019-03-25T22:43:43","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/?p=4396"},"modified":"2026-04-06T20:03:39","modified_gmt":"2026-04-07T00:03:39","slug":"comparison-of-hand-held-wemi-target-detection-algorithms","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2019\/03\/25\/comparison-of-hand-held-wemi-target-detection-algorithms\/","title":{"rendered":"Comparison of Hand-held WEMI Target Detection Algorithms"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low amounts of conductive materials can be challenging. In this paper, we compare a number of tar- get detection algorithms in the literature in terms of detection performance. In the comparison, methods are tested on two real-world data sets: one containing relatively low amounts of ground noise pollution, and the other demonstrating highly-magnetic soil interference. Results are quantitatively evaluated through receiver-operator characteristic (ROC) curves and are used to highlight the strengths and weaknesses of the compared approaches in hand-held explosive hazard detection.<\/p>\n<h2>Links:<\/h2>\n<p><a href=\"https:\/\/arxiv.org\/abs\/1903.09587\"><img decoding=\"async\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/arxiv.png\" alt=\"\u201cArXiv link of the paper\" height=\"50\" \/><\/a><a href=\"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/11012\/110120U\/Comparison-of-hand-held-WEMI-target-detection-algorithms\/10.1117\/12.2519454.short\"><img decoding=\"async\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/SPIE-logo-cmyk-e1482256584489.jpg\" alt=\"SPIE link of the paper\" height=\"50\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>C. McCurley, J. Bocinsky, and A. Zare, \u201cComparison of Hand-held WEMI Target Detection Algorithms,\u201d in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.<\/code><\/pre>\n<pre><code>@Article{McCurley2019Comparision\nTitle = {Comparison of Hand-held WEMI Target Detection Algorithms},\nAuthor = {C. McCurley and J. Bocinsky and A. Zare},\nBooktitle = {Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV},\nSeries = {Proc. SPIE},\nVolume = {11012},\nYear = {2019},\nMonth = {May},\n }\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low amounts of conductive materials can be challenging. In this paper, we compare a number of [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"off","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17,9,13],"tags":[61,399,487,695,733,801],"class_list":["post-4396","post","type-post","status-publish","format-standard","hentry","category-conference_paper","category-news","category-publication","tag-adaptive-cosine-estimator","tag-joint-orthogonal-matching-pursuits","tag-multiple-instance","tag-spectral-matched-filter","tag-target-detection","tag-wide-band-electromagnetic-induction-sensors"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/4396","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=4396"}],"version-history":[{"count":2,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/4396\/revisions"}],"predecessor-version":[{"id":16371,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/4396\/revisions\/16371"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=4396"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=4396"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=4396"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}