{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T13:35:11Z","timestamp":1777296911465,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,25]],"date-time":"2019-06-25T00:00:00Z","timestamp":1561420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Dark target detection is important for engineering applications but the existing methods do not consider the imaging environment of dark targets, such as the adjacency effect. The adjacency effect will affect the quantitative applications of remote sensing, especially for high contrast images and images with ever-increasing resolution. Further, most studies have focused on how to eliminate the adjacency effect and there is almost no research about the application of the adjacency effect. However, the adjacency effect leads to some unique characteristics for the dark target surrounded by a bright background. This paper utilizes these characteristics to assist in the detection of the dark object, and the low-high threshold detection strategy and the adaptive threshold selection method under the assumption of Gaussian distribution are designed. Meanwhile, preliminary case experiments are carried out on the crack detection of concrete slope protection. Finally, the experiment results show that it is feasible to utilize the adjacency effect for dark target detection.<\/jats:p>","DOI":"10.3390\/s19122829","type":"journal-article","created":{"date-parts":[[2019,6,25]],"date-time":"2019-06-25T10:52:31Z","timestamp":1561459951000},"page":"2829","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Dark Target Detection Method Based on the Adjacency Effect: A Case Study on Crack Detection"],"prefix":"10.3390","volume":"19","author":[{"given":"Li","family":"Yu","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yugang","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"China Energy Engineering Group Guangdong Electric Power Design Institute Company Limited, Guangzhou 510700, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijleo.2015.09.147","article-title":"Detection crack in image using Otsu method and multiple filtering in image processing techniques","volume":"127","author":"Talab","year":"2016","journal-title":"Optik"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1002\/sca.21037","article-title":"A Contrast Stretching Bilateral Closing Top-Hat Otsu Threshold Technique for Crack Detection in Images","volume":"35","author":"Sim","year":"2013","journal-title":"Scanning"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s11227-018-2622-0","article-title":"An Otsu multi-thresholds segmentation algorithm based on improved ACO","volume":"75","author":"Qin","year":"2019","journal-title":"J. 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