{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:14:46Z","timestamp":1787908486139,"version":"build-2784847793"},"reference-count":37,"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":["Information"],"abstract":"<jats:p>Traffic sign detection is critically important for intelligent transportation systems, yet persistent challenges like multi-scale variation and complex background interference severely degrade detection accuracy and real-time performance. To address these limitations, this study presents CPB-YOLOv8, an advanced multi-scale detection framework based on the YOLOv8 architecture. A Cross-Stage Partial-Partitioned Transformer Block (CSP-PTB) is incorporated into the feature extraction stage to preserve semantic information during downsampling while enhancing global feature representation. For feature fusion, a four-level bidirectional feature pyramid BiFPN integrated with a P2 detection layer significantly improves small-target detection capability. Further enhancement is achieved via an optimized loss function that balances multi-scale objective localization. Comprehensive evaluations were conducted on the TT100K, the CCTSDB, and a custom multi-scenario road image dataset capturing urban and suburban environments at 1920 \u00d7 1080 resolution. Results demonstrate compelling performance: On TT100K, CPB-YOLOv8 achieved 90.73% mAP@0.5 with a 12.5 MB model size, exceeding the YOLOv8s baseline by 3.94 percentage points and achieving 6.43% higher small-target recall. On CCTSDB, it attained a near-saturation performance of 99.21% mAP@0.5. Crucially, the model demonstrated exceptional robustness across diverse environmental conditions. Rigorous analysis on partitioned CCTSDB subsets based on weather and illumination, alongside validation using a separate self-collected dataset reserved solely for inference, confirmed strong adaptability to real-world distribution shifts and low-visibility scenarios. Cross-dataset validation and visual comparisons further substantiated the model\u2019s robustness and its effective suppression of background interference.<\/jats:p>","DOI":"10.3390\/info16090798","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T11:51:43Z","timestamp":1757937103000},"page":"798","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["CPB-YOLOv8: An Enhanced Multi-Scale Traffic Sign Detector for Complex Road Environment"],"prefix":"10.3390","volume":"16","author":[{"given":"Wei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanlan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TITS.2011.2158001","article-title":"Data-Driven Intelligent Transportation Systems: A Survey","volume":"12","author":"Zhang","year":"2011","journal-title":"IEEE Trans. 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