Application of Deep Learning in Drone Image Detection

As an important research direction in the field of computer vision, image detection is a technology that can localize and identify the image and can accurately discriminate the features of the task image, and its application to UAVs has been a hot topic in recent years. The field of algorithms of deep learning applied to image detection is progressing rapidly, however, there are still many advantages and disadvantages between various algorithms, and the application of UAV image detection focuses on different. Therefore, the research topic of this article is to compare the advantages and disadvantages of various algorithms of image detection and conduct a technical analysis of improved algorithms applied to drones. The research method of this paper is as follows: firstly, introducing the model of each algorithm, analyzing the principle, and then comparing the advantages and disadvantages of detection speed, detection accuracy, and finally choosing improved algorithms that are suitable for drone Image detection. the improved algorithms suitable for UAV image detection are selected. In terms of accuracy, Faster R-CNN and YOLO perform better compared to R-CNN and Fast R-CNN. In terms of speed, YOLO has a higher processing speed and is suitable for application scenarios that require high real-time performance. Faster R-CNN is suitable for scenarios that require high accuracy but relatively low-speed requirements. SSD performs outstandingly in handling multi-scale targets. Therefore, in the field of UAV image detection, choosing the appropriate deep learning model can balance the needs of accuracy and speed according to the application scenarios.