{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T12:52:34Z","timestamp":1781959954466,"version":"3.54.5"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Achieving accurate detection of small and occluded objects in complex natural scenes remains challenging for real-time systems. This paper presents CSS-YOLO, a lightweight detector built upon YOLOv11 that enhances multi-scale semantic consistency, channel discriminability, and local spatial modeling. CSS-YOLO integrates a cross-channel feature fusion module, a multi-branch SENetV2 channel attention, and a spatially coupled convolution block. On a self-constructed Rosa davurica Pall dataset, CSS-YOLO reaches a mean average precision (mAP) at Intersection over Union (IoU) 0.5 (mAP@0.5) of 93.5% and mAP@0.5:0.95 of 64.0%, while reducing parameters by 33.1%, Giga Floating-point Operations Per Second (GFLOPs) by 17.5%, and model size by 30.8% compared with YOLOv11 . Ablation studies quantify the contribution of each module; comparisons on pomegranate and strawberry datasets demonstrate strong cross-scenario generalization. The results indicate that CSS-YOLO offers a practical balance of accuracy and efficiency suitable for edge deployment in smart agriculture and similar real-time applications.<\/jats:p>","DOI":"10.1093\/comjnl\/bxag001","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T12:42:49Z","timestamp":1767789769000},"page":"929-944","source":"Crossref","is-referenced-by-count":0,"title":["A lightweight object detector with channel\u2013spatial optimization for small targets in complex scenes"],"prefix":"10.1093","volume":"69","author":[{"given":"Jingxin","family":"Han","sequence":"first","affiliation":[{"name":"College of Information and Computer Engineering, Northeast Forestry University , Hexing Road 26, 150040, Harbin, Heilongjiang Province 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