{"id":2,"date":"2025-05-06T06:03:48","date_gmt":"2025-05-06T06:03:48","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/?page_id=2"},"modified":"2025-12-10T21:11:58","modified_gmt":"2025-12-10T21:11:58","slug":"introduction","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/","title":{"rendered":"Introduction"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Motivation<\/h2>\n\n\n\n<p>Traditional pre-flight inspections of commercial airliners are labor-intensive, time-consuming, and prone to human error, often requiring workers to manually access hard-to-reach areas to identify dents and other defects. This process can take up to four hours, resulting in costly downtime for airlines, with potential losses averaging $10,000 per hour in missed earnings. Implementing drone-enabled inspection systems can significantly reduce inspection time to under 30 minutes while increasing accuracy and enabling remote analysis of high-resolution images, ensuring quicker detection of issues such as cracks, popped rivets, and leaks.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"604\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/pre_flight_inspection-1024x604.jpeg\" alt=\"\" class=\"wp-image-30\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/pre_flight_inspection-1024x604.jpeg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/pre_flight_inspection-300x177.jpeg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/pre_flight_inspection-768x453.jpeg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/pre_flight_inspection.jpeg 1200w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"has-text-align-center\">A manual pre-flight inspection<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"614\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/cropped-nearearthautonomy-emirates-20241231-1024x614.jpeg\" alt=\"\" class=\"wp-image-13\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/cropped-nearearthautonomy-emirates-20241231-1024x614.jpeg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/cropped-nearearthautonomy-emirates-20241231-300x180.jpeg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/cropped-nearearthautonomy-emirates-20241231-768x461.jpeg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/cropped-nearearthautonomy-emirates-20241231-1536x922.jpeg 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/cropped-nearearthautonomy-emirates-20241231.jpeg 2000w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center\">Automatic Inspection of the aircraft with a drone<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Problem Statement<\/h2>\n\n\n\n<p>The objective of our project is to develop a drone-based inspection system capable of accurately detecting dents as small as 1-5mm in depth and 1cm in width from a distance of 2-4 meters. To ensure reliable performance, our algorithm must be robust to varying aircraft types, illumination conditions, and surface textures.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/05\/dents.jpg\" alt=\"\" class=\"wp-image-31\" \/><\/figure>\n<\/div>\n\n\n<p class=\"has-text-align-center\">A plane wing with dents on it<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"650\" height=\"474\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-1.png\" alt=\"\" class=\"wp-image-76\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-1.png 650w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-1-300x219.png 300w\" sizes=\"auto, (max-width: 650px) 100vw, 650px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"has-text-align-center\">Part of a fuselage with multiple dents on it<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Challenges<\/h2>\n\n\n\n<p>The main challenge in detecting dents is that photographic images alone can be unreliable under certain viewing conditions. Consider a simple example: a metal plate with several dents. The two images below show the same plate photographed from different angles. In the first image, the dents are clearly visible, but in the second, changes in illumination and surface reflections render them nearly invisible.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"434\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-2-1024x434.png\" alt=\"\" class=\"wp-image-87\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-2-1024x434.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-2-300x127.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-2-768x325.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-2-1536x650.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-2.png 1696w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center\">Photos of the same plate with dents with different viewing angles and illumination<br><\/p>\n\n\n\n<p>To address this problem, we employ active illumination techniques. Specifically, we project a straight laser line onto the aircraft surface. Any dents will distort the laser line, and by detecting these deformations using classical computer vision methods, we can identify candidate dent locations.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"948\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-3-1024x948.png\" alt=\"\" class=\"wp-image-88\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-3-1024x948.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-3-300x278.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-3-768x711.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/wp-content\/uploads\/sites\/132\/2025\/12\/image-3.png 1056w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center\">By projecting laser line onto the aircraft, we can identify candidate dent locations<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Motivation Traditional pre-flight inspections of commercial airliners are labor-intensive, time-consuming, and prone to human error, often requiring workers to manually access hard-to-reach areas to identify dents and other defects. This process can take up to four hours, resulting in costly downtime for airlines, with potential losses averaging $10,000 per hour in missed earnings. Implementing drone-enabled &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Introduction&#8221;<\/span><\/a><\/p>\n","protected":false},"author":249,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Introduction - Finding Dents and Dings With a Drone using Structured Light<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team5\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introduction - Finding Dents and Dings With a Drone using Structured Light\" \/>\n<meta property=\"og:description\" content=\"Motivation Traditional pre-flight inspections of commercial airliners are labor-intensive, time-consuming, and prone to human error, often requiring workers to manually access hard-to-reach areas to identify dents and other defects. 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