Research Information System
5th International Conference on Agriculture Digitalization and Organic Production, ADOP 2025, Barnaul, Russia, 3 - 06 June 2025, vol.453 SIST, pp.151-162, (Full Text)
Using unmanned aerial vehicles (UAVs) in modern agriculture faces the problem of overcoming obstacles. One of the most widespread types of these obstacles on the agricultural land is powerlines and aerial communication lines, which are crucial for agriculture itself as well as for other economic activities, and thus cannot be removed from the agricultural lands. The initial subtask in overcoming poles is the detection of such objects. Recent neural network detection architectures, such as YOLO, have shown promising results in general object detection tasks, however, the results of comparative studies of YOLO architectures in a specific task of pole detection are not presented in scientific literature. In this work, we present results of a comparative study of a set of YOLO architectures’ performance on a custom dataset of powerlines and aerial communication lines poles on the agricultural land obtained using a UAV. The dataset consists of 3508 images with 1691 wooden poles and 1750 concrete poles. We consider five recent YOLO architectures from v8 to v12. Comparative analysis of the considered architectures has shown that YOLOv11 achieved the best performance in average according to recall (0.765), precision (0.798), mAP@50 (0.809) and mAP@50–90 (0.484) metrics. These results, along with the least required computational resources (6.5 GFLOPS), make YOLOv11 the most appropriate architecture for pole detection on the agricultural land.