{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:03:16Z","timestamp":1783180996701,"version":"3.54.6"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Agricultural Industry Technology System","award":["JATS[2022]483"],"award-info":[{"award-number":["JATS[2022]483"]}]},{"name":"Jiangsu Agricultural Industry Technology System","award":["NJ2021-38"],"award-info":[{"award-number":["NJ2021-38"]}]},{"name":"Jiangsu Agricultural Industry Technology System","award":["NJ [2022]07"],"award-info":[{"award-number":["NJ [2022]07"]}]},{"name":"the project of modern agricultural machinery equipment and technology demonstration and promotion of Jiangsu province","award":["JATS[2022]483"],"award-info":[{"award-number":["JATS[2022]483"]}]},{"name":"the project of modern agricultural machinery equipment and technology demonstration and promotion of Jiangsu province","award":["NJ2021-38"],"award-info":[{"award-number":["NJ2021-38"]}]},{"name":"the project of modern agricultural machinery equipment and technology demonstration and promotion of Jiangsu province","award":["NJ [2022]07"],"award-info":[{"award-number":["NJ [2022]07"]}]},{"name":"the project of modern agricultural machinery equipment and technology innovation demonstration of Nanjing City","award":["JATS[2022]483"],"award-info":[{"award-number":["JATS[2022]483"]}]},{"name":"the project of modern agricultural machinery equipment and technology innovation demonstration of Nanjing City","award":["NJ2021-38"],"award-info":[{"award-number":["NJ2021-38"]}]},{"name":"the project of modern agricultural machinery equipment and technology innovation demonstration of Nanjing City","award":["NJ [2022]07"],"award-info":[{"award-number":["NJ [2022]07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to remedy the defects of single sensor in robustness, accuracy, and redundancy of target detection, this paper proposed a method for detecting obstacles in farmland based on the information fusion of a millimeter wave (mmWave) radar and a camera. Combining the advantages of the mmWave radar in range and speed measurement and the camera in type identification and lateral localization, a decision-level fusion algorithm was designed for the mmWave radar and camera information, and the global nearest neighbor method was used for data association. Then, the effective target sequences of the mmWave radar and the camera with successful data association were weighted to output, and the output included more accurate target orientation, longitudinal speed, and category. For the unassociated sequences, they were tracked as new targets by using the extended Kalman filter algorithm and were processed and output during the effective life cycle. Lastly, an experimental platform based on a tractor was built to verify the effectiveness of the proposed association detection method. The obstacle detection test was conducted under the ROS environment after solving the external parameters of the mmWave radar and the internal and external parameters of the camera. The test results show that the correct detection rate of obstacles reaches 86.18%, which is higher than that of a single camera with 62.47%. Furthermore, through the contrast experiment of the sensor fusion algorithms, the detection accuracy of the decision level fusion algorithm was 95.19%, which was higher than 4.38% and 6.63% compared with feature level and data level fusion, respectively.<\/jats:p>","DOI":"10.3390\/s23010230","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T03:05:56Z","timestamp":1672110356000},"page":"230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-Objective Association Detection of Farmland Obstacles Based on Information Fusion of Millimeter Wave Radar and Camera"],"prefix":"10.3390","volume":"23","author":[{"given":"Pengfei","family":"Lv","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Agricultural Machinery Information Center, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7643-6183","authenticated-orcid":false,"given":"Jinlin","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.compag.2007.06.004","article-title":"An Agent of Behaviour Architecture for Unmanned Control of a Farming Vehicle","volume":"60","author":"Ribeiro","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fue, K., Porter, W., Barnes, E., Li, C.Y., and Rains, G. (2020). Autonomous Navigation of a Center-articulated and Hydrostatic Transmission Rover Using a Modified Pure Pursuit Algorithm in a Cotton Field. Sensors, 20.","DOI":"10.3390\/s20164412"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., and Dragana, C. (2020). Advanced UAV-WSN System for Intelligent Monitoring in Precision Agriculture. Sensors, 20.","DOI":"10.3390\/s20030817"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107259","DOI":"10.1016\/j.compag.2022.107259","article-title":"Multiple Object Tracking in Farmland Based on Fusion Point Cloud Data","volume":"200","author":"Ji","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11226","DOI":"10.3390\/s101211226","article-title":"Sensor Architecture and Task Classification for Agricultural Vehicles and Environments","volume":"10","year":"2010","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, T., Huang, Z.H., You, W.J., Lin, J.T., Tang, X.L., and Huang, H. (2020). An Autonomous Fruit and Vegetable Harvester with a Low-cost Gripper Using a 3D Sensor. Sensors, 20.","DOI":"10.3390\/s20010093"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yeong, D., Velasco-Hernandez, G., Barry, J., and Walsh, J. (2021). Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review. Sensors, 21.","DOI":"10.20944\/preprints202102.0459.v1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.13031\/jash.22.11260","article-title":"Object Detection for Agricultural and Construction Environments Using an Ultrasonic Sensor","volume":"22","author":"Dvorak","year":"2016","journal-title":"J. Agric. Saf. Health"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xue, J., Cheng, F., Li, Y., Song, Y., and Mao, T. (2022). Detection of Farmland Obstacles Based on an Improved YOLOv5s Algorithm by Using CIoU and Anchor Box Scale Clustering. Sensors, 22.","DOI":"10.3390\/s22051790"},{"key":"ref_10","unstructured":"Nashashibi, A., and Ulaby, F.T. (2001, January 8\u201313). Millimeter Wave Radar Detection of Partially Obscured Targets. Proceedings of the IEEE Antennas and Propaga-tion Society International Symposium. 2001 Digest. Held in Conjunction with: USNC\/URSI National Radio Science Meeting (Cat. No.01CH37229), Boston, MA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106409","DOI":"10.1016\/j.compag.2021.106409","article-title":"Obstacle Detection and Recognition in Farmland Based on Fusion Point Cloud Data","volume":"189","author":"Ji","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2018.02.009","article-title":"Multi-feature Fusion Tree Trunk Detection and Orchard Mobile Robot Localization Using Camera\/Ultrasonic Sensors","volume":"147","author":"Chen","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106382","DOI":"10.1016\/j.compag.2021.106382","article-title":"A System for Plant Detection Using Sensor Fusion Approach Based on Machine Learning Model","volume":"189","author":"Maldaner","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","first-page":"20","article-title":"Trunk Detection Based on Laser Radar and Vision Data Fusion","volume":"11","author":"Xue","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, Z.Q., Zhang, F.K., Chang, S., Liu, Y.Y., Wu, H.C., and Feng, Z.Y. (2022). MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review. Sensors, 22.","DOI":"10.3390\/s22072542"},{"key":"ref_16","first-page":"609","article-title":"Moving Object Tracking Based on Millimeter-wave Radar and Vision Sensor","volume":"21","author":"Huang","year":"2018","journal-title":"J. Appl. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8992","DOI":"10.3390\/s110908992","article-title":"Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-road Obstacle Detection Applications","volume":"11","author":"Wang","year":"2011","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"044102","DOI":"10.1063\/1.5093279","article-title":"Unifying Obstacle Detection, Recognition, and Fusion Based on Millimeter Wave Radar and RGB-depth Sensors for the Visually Impaired","volume":"90","author":"Long","year":"2019","journal-title":"Rev. Sci. Instrum."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1109\/TITS.2016.2533542","article-title":"On-Road Vehicle Detection and Tracking Using MMW Radar and Monovision Fusion","volume":"17","author":"Wang","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nobis, F., Geisslinger, M., Weber, M., Betz, J., and Lienkamp, M. (2019). A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection. 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), IEEE.","DOI":"10.1109\/SDF.2019.8916629"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, X., Du, J., Gao, J., and Wang, W. (2018, January 18\u201320). Pedestrian Detection Based on Fusion of Millimeter Wave Radar and Vision. Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition, Beijing, China.","DOI":"10.1145\/3268866.3268868"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chang, S., Zhang, Y., Zhang, F., Zhao, X., Huang, S., Feng, Z., and Wei, Z. (2020). Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor. Sensors, 20.","DOI":"10.3390\/s20040956"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/LSP.2012.2220350","article-title":"Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks","volume":"19","author":"Masazade","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A Flexible New Technique for Camera Calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/230\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:51:26Z","timestamp":1760147486000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,26]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010230"],"URL":"https:\/\/doi.org\/10.3390\/s23010230","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,26]]}}}