{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T17:02:31Z","timestamp":1789578151196,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371271"],"award-info":[{"award-number":["62371271"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62371271"],"award-info":[{"award-number":["62371271"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: To address the challenges in ground-penetrating radar (GPR) B-scan profiles\u2014where targets typically exhibit hyperbolic signatures, small scales, low contrast, and severe background clutter\u2014this paper proposes an efficient and accurate target detection algorithm based on YOLOv8n, termed YOLOv8n-PCPG. (2) Methods: First, a Pinwheel-shaped Convolution (PSConv) is integrated into the backbone to capture directional hyperbolic boundaries. Second, a lightweight CSP-PMSFA module replaces the standard C2f structures in the neck, reducing computational redundancy while preserving feature integrity. Finally, a structurally reparameterized GC-downsampling module is introduced to enhance cross-channel feature interaction without adding inference latency. (3) Extensive experiments on a self-constructed GPR B-scan dataset show that the proposed model achieves a Precision of 97.2%, a Recall of 97.3%, an mAP@0.5 of 98.8%, and an mAP@0.75 of 84.3%. Compared with the baseline YOLOv8n, the parameter count and GFLOPs are reduced by 13.0% and 7.4%, respectively, while achieving a real-time detection speed of 155.78 FPS, outperforming existing mainstream detectors. (4) Conclusions: These results demonstrate that YOLOv8n-PCPG achieves a strong trade-off between detection accuracy and computational efficiency, providing effective technical support for subsurface hyperbolic signature identification.<\/jats:p>","DOI":"10.3390\/s26185872","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:07:29Z","timestamp":1789574849000},"page":"5872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Feature-Enhanced Deep Learning Network for GPR Hyperbolic Target Detection in B-Scan Profiles"],"prefix":"10.3390","volume":"26","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8039-8650","authenticated-orcid":false,"given":"Qinghe","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/LGRS.2020.2995670","article-title":"Penetration properties of ground penetrating radar waves through rebar grids","volume":"18","author":"Liu","year":"2020","journal-title":"IEEE Geosci. 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