{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:41:02Z","timestamp":1760402462958,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Natural Science Foundation of China","award":["61976098"],"award-info":[{"award-number":["61976098"]}]},{"name":"Technology Development Foundation of Quanzhou City","award":["2020C067"],"award-info":[{"award-number":["2020C067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Due to the arbitrariness of the drone\u2019s shooting angle of view and camera movement and the limited computing power of the drone platform, pedestrian detection in the drone scene poses a greater challenge. This paper proposes a new convolutional neural network structure, SMYOLO, which achieves the balance of accuracy and speed from three aspects: (1) By combining deep separable convolution and point convolution and replacing the activation function, the calculation amount and parameters of the original network are reduced; (2) by adding a batch normalization (BN) layer, SMYOLO accelerates the convergence and improves the generalization ability; and (3) through scale matching, reduces the feature loss of the original network. Compared with the original network model, SMYOLO reduces the accuracy of the model by only 4.36%, the model size is reduced by 76.90%, the inference speed is increased by 43.29%, and the detection target is accelerated by 33.33%, achieving minimization of the network model volume while ensuring the detection accuracy of the model.<\/jats:p>","DOI":"10.3390\/fi14010021","type":"journal-article","created":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T03:41:32Z","timestamp":1641440492000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios"],"prefix":"10.3390","volume":"14","author":[{"given":"Weiwei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"},{"name":"Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"},{"name":"Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"},{"name":"Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenghui","family":"Zhen","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"},{"name":"Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","unstructured":"Li, H., Wu, Z., and Zhang, J. 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