基于改进YOLOv5s的松科球果目标检测与定位

打开文本图片集

关键词：松科球果；目标检测；目标定位；YOLOv5s算法；双目深度相机 中图分类号：S791.24；TP391.4；TP18 文献标识码：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

松科球果作为一种重要的林业资源，因其在食品、医药和化工领域的广泛应用而备受关注[1]。（剩余16909字）