METHODS OF COLLECTING AND PROCESSING IMAGES OBTAINED USING UAVs TO DETECT SPECIFIED OBJECTS

Abstract

This study introduces a novel approach for identifying and quantifying specific structural objects, using fruit as an example, through images captured by drones in real time. The approach involves a series of steps: dynamically capturing images of the specified objects in 3D space using a fleet of drones, synchronizing video feeds from different drones, and finally, detecting and counting the objects. During the detection phase, a new version of the YOLOv5 model, YOLOv5-v1, was trained on a custom dataset of apple images to learn the features that differentiate apples from their surroundings. The initial size of the source network’s binding block was adjusted to avoid misidentifying small objects in the image background, thereby improving counting accuracy. The authors conducted computational experiments on a dataset of apple images they created to assess the approach’s effectiveness. The synchronization of video streams was assessed using the SSIM index, which ranged from 0.79 to 0.92, with an average value of 0.87, and the PSNR index, which ranged from 22 to 39. These values indicate the high efficiency of the developed synchronization method and the high quality of the resulting combined images. The results on the test dataset showed that the improved model could effectively identify fruits captured by the drone camera, with recall, precision, mAP, and F1-score of 92.13%, 84.59%, 87.94%, and 89.02%, respectively. The proposed approach was also compared with several other cutting-edge models, YOLOv3, YOLOv4, and YOLOv5, and was found to be superior in accuracy and speed. Our model’s average recognition speed was 0.015 seconds per video frame (66.7 frames/s), which was 1.13 and 3.53 times faster than the YOLOv4, and YOLOv3 networks, respectively. The average reliability index for detecting and counting fruit was 86.75%. These results demonstrate the effectiveness of using drone-captured images to detect specified objects. Future research could also explore including additional features, such as fruit shape, by training the model on a larger dataset.