{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:26:11Z","timestamp":1768415171584,"version":"3.49.0"},"reference-count":37,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"name":"horizontal project of China Agricultural University","award":["202405510810372"],"award-info":[{"award-number":["202405510810372"]}]},{"name":"horizontal project of China Agricultural University","award":["202405510810535"],"award-info":[{"award-number":["202405510810535"]}]},{"name":"S&T Progrogram of Hebei","award":["22375411D"],"award-info":[{"award-number":["22375411D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p> Wheat head identification and localization are crucial for field management and yield estimation. Traditional wheat head detection relies on labor-intensive manual counting. To address this issue, this study establishes a densely populated wheat head dataset and correspondingly proposes an efficient, rapid real-time model of CB-YOLO, which incorporates a Convolutional Block Attention Module (CBAM) mechanism (a sophisticated fusion of spatial attention mechanism and channel attention mechanism) so as to integrate channel and spatial features. Also, with an incorporated Bidirectional Feature Pyramid Network (BiFPN) to the neck, crowded wheat head detection was enhanced by establishing connections from the backbone to the bottom-up path. This enhancement surpasses the performance of the basic YOLOv5L model, achieving [Formula: see text] of 94.3% and [Formula: see text] of 53.9%. Among 10 classical detection models, CB-YOLO has a relatively small parameter size of 44[Formula: see text]Mb and high computing speed of 108[Formula: see text]G FLOPs. Additionally, ablation experiments demonstrate that compared to YOLOv5L without CBAM and BiFPN, the feature fusion module improves [Formula: see text] from 93.2% to 94.3%, indicating its effectiveness in enhancing detection performance. In conclusion, CB-YOLO exhibits outstanding identification performance, showing its feasibility in practical agricultural applications, and future work will focus on improving its localization ability. <\/jats:p>","DOI":"10.1142\/s0218126625500793","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T02:39:31Z","timestamp":1725590371000},"source":"Crossref","is-referenced-by-count":3,"title":["CB-YOLO: Dense Object Detection of YOLO for Crowded Wheat Head Identification and Localization"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0446-7703","authenticated-orcid":false,"given":"Wenzhuo","family":"Chen","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P. R. China"},{"name":"North China Institute of Science and Technology, Langfang 065201, P. R. 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