基于改進(jìn)YOLOv5s的松科球果目標(biāo)檢測(cè)與定位

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關(guān)鍵詞：松科球果；目標(biāo)檢測(cè)；目標(biāo)定位；YOLOv5s算法；雙目深度相機(jī) 中圖分類號(hào)：S791.24；TP391.4；TP18 文獻(xiàn)標(biāo)識(shí)碼：A DOI：10.7525/j.issn.1006-8023.2025.04.015

Abstract：Traditional methods for harvesting pinecone speciesface challenges such as low eficiency，high risks，and uncontrollable costs.To addressreal-time recognition and localization in automated pinecone harvesting，we proposed animproved YOLOv5s-7.0（youonlylookonce）objectdetectionmodeland constructabinoculardepthcamera-based detectionand localization network.To improvetheaccuracyand eficiencyof object detection，theYOLOv5s model was improved byembeddng partial convolutions （PConv）into the neck module's multi-branch stacked structure to enhance sparsefeature processng capability，improve robustness，and reduce feature redundancy incomplex scenarios of pinecones.Aditionally，the simple atention mechanism （SimAM）was integrated at deep backbone layers and backboneneck connections tooptimizethe model’sfeatureextractionabilityand information transmision eficiencyincomplex backgrounds without significantparameter increases.To meet therequirements of efficient detectionand localization，a target detection and real-time localizationcode was developedusing binocular vision principlesand the improved YOLOv5s model，and a pinecone detection and localization system was constructed through depth matching.Based on theconstructed datasetof Pinus sylvestris var.mongolicacones fromthe Greater Khingan Mountains and Pinus koraiensis cones from the Lesser Khingan Mountains，the improved YOLOv5s model achieved a precision of 96.8% ，a recall of 94.0% ， and an average precision （AP） of 96. 3% in target detection tasks. The proposed pinecone detection and localization system demonstrated mean absolute errors of （ ）.644cm ，0 ?620cm ，and 0.740 cm along the x ，y-，and Z -axes， respectively. Under front，side，and backlighting conditions，the localization success rate reached 93.3% ，while in lowlight environments，it maintained a success rate of 83.3% . Other performance indicators，including field of view，meet the operational requirements for pinecone harvesting.The proposed pinecone detection and localization system provides a reliable solution for real-time target detection and localization problems in mechanized pinecone harvesting.

Keywords：Pinecone;target detection;target localization;YOLOv5s algorithm;binocular depth camera

0 引言

松科球果作為一種重要的林業(yè)資源，因其在食品、醫(yī)藥和化工領(lǐng)域的廣泛應(yīng)用而備受關(guān)注[1]。（剩余16909字）