{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T08:16:35Z","timestamp":1782461795488,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012774","name":"Innovation Fund Denmark","doi-asserted-by":"publisher","award":["9065-00036B"],"award-info":[{"award-number":["9065-00036B"]}],"id":[{"id":"10.13039\/100012774","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision\/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases.<\/jats:p>","DOI":"10.3390\/s22103608","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T21:52:11Z","timestamp":1652219531000},"page":"3608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Anomaly Detection for Agricultural Vehicles Using Autoencoders"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1584-0857","authenticated-orcid":false,"given":"Esma","family":"Mujkic","sequence":"first","affiliation":[{"name":"Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark"},{"name":"AGCO A\/S, 8930 Randers, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9212-2544","authenticated-orcid":false,"given":"Mark P.","family":"Philipsen","sequence":"additional","affiliation":[{"name":"Visual Analysis and Perception Lab, Department of Architecture, Design, and Media Technology, Aalborg University, 9000 Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7584-5209","authenticated-orcid":false,"given":"Thomas B.","family":"Moeslund","sequence":"additional","affiliation":[{"name":"Visual Analysis and Perception Lab, Department of Architecture, Design, and Media Technology, Aalborg University, 9000 Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6214-9818","authenticated-orcid":false,"given":"Martin P.","family":"Christiansen","sequence":"additional","affiliation":[{"name":"AGCO A\/S, 8930 Randers, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2265-0031","authenticated-orcid":false,"given":"Ole","family":"Ravn","sequence":"additional","affiliation":[{"name":"Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Development status and trend of agricultural robot technology","volume":"14","author":"Jin","year":"2021","journal-title":"Int. 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