{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:45:22Z","timestamp":1760060722483,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This paper proposes DCGAN-YOLOv8n, an integrated framework that significantly advances small-sample target detection by synergizing generative adversarial feature enhancement with multi-scale representation learning. The model\u2019s core contribution lies in its novel adversarial feature enhancement module (AFEM), which leverages conditional generative adversarial networks to reconstruct discriminative multi-scale features while effectively mitigating mode collapse. Furthermore, the architecture incorporates a deformable multi-scale feature pyramid that dynamically fuses generated high-resolution features with hierarchical semantic representations through an attention mechanism. The proposed triple marginal constraint optimization jointly enhances intra-class compactness and inter-class separation, thereby structuring a highly discriminative feature space. Extensive experiments on the NWPU VHR-10 dataset demonstrate state-of-the-art performance, with the model achieving an mAP50 of 90.46% and an mAP50-95 of 57.06%, representing significant improvements of 4.52% and 4.08% over the baseline YOLOv8n, respectively. These results validate the framework\u2019s effectiveness in addressing critical challenges of feature representation scarcity and cross-scale adaptation in data-limited scenarios.<\/jats:p>","DOI":"10.3390\/computers14090389","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T16:27:33Z","timestamp":1757953653000},"page":"389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DCGAN Feature-Enhancement-Based YOLOv8n Model in Small-Sample Target Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Peng","family":"Zheng","sequence":"first","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenhao","family":"Ye","sequence":"additional","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8997-4180","authenticated-orcid":false,"given":"Shuhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6677-0971","authenticated-orcid":false,"given":"Jinyin","family":"Bai","sequence":"additional","affiliation":[{"name":"Graduate School, National University of Defense Technology, Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111283","DOI":"10.1016\/j.patcog.2024.111283","article-title":"Text Generation and Multi-Modal Knowledge Transfer for Few-Shot Object Detection","volume":"161","author":"Du","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125905","DOI":"10.1016\/j.eswa.2024.125905","article-title":"Orthogonal Progressive Network for Few-Shot Object Detection","volume":"264","author":"Wang","year":"2025","journal-title":"Expert Syst. 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