Classification of Tree Species in Transmission Line Corridors Based on YOLO v7

Abstract

The effective control of trees in transmission line corridors is crucial to mitigate the damage that they can cause to transmission lines. Investigating trees in these corridors presents a significant challenge, particularly in classifying individual tree species. Although the current deep learning model can segment single tree species, it exhibits low recognition accuracy in areas with dense forest canopies. The detection speed is also subject to limitations. To address these challenges, this study relies on aerial multispectral images obtained from drones as the primary data source. The process begins by extracting single tree crowns and establishing a sample dataset, divided in a 9:1 ratio into training and verification sets. Subsequently, the training set undergoes iterative parameter training using the YOLO v7 network. Once optimal parameters are obtained, the system outputs information on individual tree types. The verification sample set is then employed to assess the accuracy. Simultaneously, the YOLO v4 network model is applied to the same data, and the training results of the YOLO v7 network are compared and analyzed, revealing peak accuracy of 85.42% in recognizing single tree species. This approach provides an effective solution, offering reliable data for the in-depth investigation of trees in transmission line corridors and the accurate monitoring of concealed tree hazards.