Abstract: As an important branch of renewable energy,the photovoltaic industry has ushered in explosive growth under the background of “double carbon”.The traditional means of progress management during the construction period of photovoltaic projects are no longer sufficient to meet the needs of on-site managers,and there is an urgent need for an accurate and efficient construction progress automation management method.Current target detection algorithms are limited by the difficulty of feature extraction in complex environments and low detection accuracy,and can not be applied to string identification in photovoltaic fields.Based on this,this paper proposes a PV field construction progress detection technology: firstly,use a drone to take remote sensing images of the PV field,and automatically stitch and crop the image data; secondly,use the improved YOLOv11 model to identify the images,identify the objects including PV panels and racks,and compare the coordinates of the identified objects with the CAD layout plan; finally,generate the accurate construction progress information for site management.Relying on the data collected from the actual project for analysis and verification,the results show that,the progress recognition error of this technology for PV panels and brackets is5.2% and 9.5% respectively,which is smaller than the real results,and considering the improvement of the generalisation ability of the model after iteration,it can meet the requirements for practical use.The improved YOLOv11 model used in this technology enhances the detection performance in complex environments by introducing BiFPN and SEAM modules,and achieves 91.8%mAP@0.5,93.4%accuracy and 89.8%recall,which are 1.6%,2.2%,1.2%and 4.2%,3.6%,3.1%respectively compared with YOLOv11 and YOLOv10 models.

Key words: progress management, YOLOv11, BiFPN, SEAM, photovoltaic