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This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective distortion with a pinhole camera. Additionally, we highlight the crucial role of knowledge about occluded features. Training the CNN with such knowledge slightly reduces pixel precision, but enables the prediction of 3 times more features, including those that are not initially visible, resulting in an overall better performing 6D system. Notably, we reveal that the data augmentation technique of<jats:italic>scale<\/jats:italic>can interfere with pixel precision when used during training. These findings are crucial for the entire system, which leverages the Solve Perspective-N-Point (Solve-PnP) algorithm, achieving 6D pose accuracy within 1<jats:inline-formula><jats:alternatives><jats:tex-math>$$^\\circ$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:msup><mml:mrow\/><mml:mo>\u2218<\/mml:mo><\/mml:msup><\/mml:math><\/jats:alternatives><\/jats:inline-formula>and 7\u00a0cm at distances ranging from 7.5 to 35 m from the camera. Moreover, this solution operates in real-time, achieving sub-10ms processing times on a desktop PC.<\/jats:p>","DOI":"10.1007\/s00521-023-09094-8","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T20:02:37Z","timestamp":1698782557000},"page":"1261-1281","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An analysis of precision: occlusion and perspective geometry\u2019s role in 6D pose estimation"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5649-3092","authenticated-orcid":false,"given":"Jeffrey","family":"Choate","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9635-6022","authenticated-orcid":false,"given":"Derek","family":"Worth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4271-2132","authenticated-orcid":false,"given":"Scott","family":"Nykl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6417-3715","authenticated-orcid":false,"given":"Clark","family":"Taylor","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4982-9859","authenticated-orcid":false,"given":"Brett","family":"Borghetti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9778-0813","authenticated-orcid":false,"given":"Christine","family":"Schubert Kabban","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"9094_CR1","doi-asserted-by":"crossref","unstructured":"Anderson James D, Nykl Scott, Wischgoll Thomas (2019) Augmenting flight imagery from aerial refueling. 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