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We present DPCNet, a single-stage detector that combines dual-path cross perception with deep and shallow feature interaction and a decoupled detection head. The Dual-Path Cross Perception block separates a detail stream and a semantic stream and performs gated bidirectional fusion, preserving edges while enriching context. The Deep and Shallow Feature Interaction block aligns features across levels through dynamic up-sampling and down-sampling and similarity-guided masking, which strengthens cross-scale consistency. The Dual-Path Decoupled Detection Head keeps classification and regression separate yet enables lightweight cross-branch channel and spatial guidance, and bounding-box regression adopts a geometry-sensitive Shape-IoU loss. Experiments on VisDrone2019 and HIT-UAV show consistent gains over the YOLO11n baseline: DPCNet improves mAP@0.5 by 2.0% and 5.1%, respectively, with higher precision and recall, especially for small, dense, low-light, and occluded targets. Despite modest computational overhead from cross-path interactions, the parameter count is reduced by about 45%, indicating a compact and robust solution for small object detection in challenging UAV scenarios.<\/jats:p>","DOI":"10.1371\/journal.pone.0344091","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:51:31Z","timestamp":1773424291000},"page":"e0344091","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["DPCNet: A dual path cross perception network for small object detection in UAV imagery"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0824-9488","authenticated-orcid":true,"given":"Linfeng","family":"Jia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6593-300X","authenticated-orcid":true,"given":"Yafeng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"issue":"6","key":"pone.0344091.ref001","article-title":"A survey of small object detection based on deep learning in aerial images","volume":"58","author":"W Hua","year":"2025","journal-title":"Artif Intell Rev"},{"issue":"1","key":"pone.0344091.ref002","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3390\/rs16010149","article-title":"A survey of object detection for UAVs Based on Deep Learning","volume":"16","author":"G Tang","year":"2023","journal-title":"Remote Sens"},{"key":"pone.0344091.ref003","first-page":"200561","article-title":"Small object detection: a comprehensive survey on challenges, techniques and real-world applications","volume":"27","author":"M Nikouei","year":"2025","journal-title":"Intell Syst Appl"},{"issue":"4","key":"pone.0344091.ref004","doi-asserted-by":"crossref","first-page":"6551","DOI":"10.3934\/mbe.2023282","article-title":"Deep learning-based small object detection: a survey","volume":"20","author":"Q Feng","year":"2023","journal-title":"Math Biosci Eng"},{"issue":"16","key":"pone.0344091.ref005","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.3390\/rs16163057","article-title":"SOD-YOLO: small-object-detection algorithm based on improved YOLOv8 for UAV images","volume":"16","author":"Y Li","year":"2024","journal-title":"Remote Sens"},{"issue":"5","key":"pone.0344091.ref006","doi-asserted-by":"crossref","first-page":"338","DOI":"10.3390\/drones9050338","article-title":"ST-YOLO: An Enhanced Detector of Small Objects in Unmanned Aerial Vehicle Imagery","volume":"9","author":"H Yan","year":"2025","journal-title":"Drones"},{"issue":"8","key":"pone.0344091.ref007","doi-asserted-by":"crossref","first-page":"557","DOI":"10.3390\/drones9080557","article-title":"Open-vocabulary object detection in UAV imagery: a review and future perspectives","volume":"9","author":"Y Zhou","year":"2025","journal-title":"Drones"},{"issue":"16","key":"pone.0344091.ref008","doi-asserted-by":"crossref","first-page":"5200","DOI":"10.3390\/s25165200","article-title":"EMFE-YOLO: a lightweight small object detection model for UAVs","volume":"25","author":"C Yang","year":"2025","journal-title":"Sensors (Basel)"},{"issue":"8","key":"pone.0344091.ref009","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.3390\/s25082534","article-title":"YOLO-MARS: An Enhanced YOLOv8n for Small Object Detection in UAV Aerial Imagery","volume":"25","author":"G Zhang","year":"2025","journal-title":"Sensors (Basel)"},{"issue":"6","key":"pone.0344091.ref010","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"39","author":"S Ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0344091.ref011","unstructured":"Redmon J, Farhadi A. 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