Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning

Abstract

Power line detection is necessary for the safe flight of low-flying UAVs (Unmanned Aerial Vehicles). This paper deals with the power line recognition problem for the safety of agricultural spraying drones in agricultural environments. The dataset of power lines was obtained in an agricultural environment. The training dataset was constructed by labeling powerlines with bounding boxes of 6 sizes, ranging from 0.03 to 0.15 times the image. The model used for training was the tiny-YOLOv3 model. The model was verified using the mean average precision (mAP), which was used to verify the object recognition performance. Depending on the six sizes of bounding boxes, the mAPs were evaluated to be 70.22, 94.00, 86.75, 68.87, 61.65, and 53.40, respectively. The mAP was the highest at the bounding box of 0.05 times the image size, and it was confirmed that this size is most suitable for power line detection. The real-time frames per second (FPS) results of power lines detection are on average 12.5. This paper shows that the location detection of power lines is possible in real-time using deep-learning techniques with embedded systems.