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However, models trained on a labeled source domain often degrade on unseen cameras due to shifts in appearance, viewpoint, and density statistics. We propose MDANet, a deployment\u2010oriented framework for cross\u2010domain crowd counting that performs complementary alignment at three levels while keeping test\u2010time inference identical to a lightweight backbone. At the data level, Fourier Amplitude Mix reduces camera\u2010dependent style gaps by mixing low\u2010frequency amplitudes. At the feature level, global\u2013local High\u2010Entropy Adversarial Regularization suppresses domain\u2010discriminative cues under spatial heterogeneity. At the domain level, Density\u2010Conditional Alignment modulates alignment strength according to predicted density to mitigate congestion\u2010dependent errors. Extensive experiments show that MDANet achieves competitive or state\u2010of\u2010the\u2010art accuracy with a favorable accuracy\u2010efficiency trade\u2010off, and additional evaluations under common stream degradations confirm its stability for edge deployment.<\/jats:p>","DOI":"10.1002\/itl2.70239","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T23:55:49Z","timestamp":1771372549000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>MDANet<\/scp>\n                    : Multi\u2010Level Domain Alignment for Edge\u2010Ready Crowd Counting in\n                    <scp>IoT<\/scp>\n                    Camera Networks"],"prefix":"10.1002","volume":"9","author":[{"given":"Xiaoan","family":"Bao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci\u2010Tech University  Hangzhou China"}]},{"given":"Chuanlong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci\u2010Tech University  Hangzhou China"}]},{"given":"Xiaomei","family":"Tu","sequence":"additional","affiliation":[{"name":"College of Urban and Rural Construction, Guangsha University  Dongyang China"}]},{"given":"Biao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci\u2010Tech University  Hangzhou China"}]},{"given":"Mingyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci\u2010Tech University  Hangzhou China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8043-2007","authenticated-orcid":false,"given":"Qingqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine Chinese Academy of Sciences  Hangzhou China"}]},{"given":"Na","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci\u2010Tech University  Hangzhou China"}]}],"member":"311","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-86247-w"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01901"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00624"},{"key":"e_1_2_9_5_1","first-page":"589","article-title":"Single\u2010Image Crowd Counting via Multi\u2010Column Convolutional Neural Network","author":"Zhang Y.","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern 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