针对复杂场景下荔枝采摘的目标检测算法研究OA

Accurate identification and harvesting of litchi fruits are crucial for achieving mechanized and automated harvesting.Due to the small size and dense growth of litchi fruits,traditional object detection algorithms struggle to achieve efficient recognition.This paper proposed the YOLOv8-SC algorithm based on the YOLOv8 model,which integrated the Swin Transformer into the backbone network to enhance small object detection performance.A shared-parameter detection head was designed to replace the original decoupled head,achieving model lightweighting.Additionally,the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm was employed to segment litchi growth patterns,providing guidance for harvesting strategies.Experimental results show that YOLOv8-SC achieves a precision of 88.5%,recall of 76.3%,F1 score of 0.819,mean average precision(mAP@0.5)of 82.1%,and mAP@0.5:0.95 of 58.2%in complex scenes,with frame rates increased to 119 frames/s.On the Jetson Xavier NX,the real-time frame rate was improved to 26 frames/s,demonstrating significant performance optimization.In field tests conducted at a litchi orchard in Lianjiang,Guangdong,the DBSCAN algorithm exhibited excellent target clustering performance,providing a reliable basis for harvesting planning.