Contrasting YOLO series and CenterNet detectors for poppy detection in different environments

As the main drug original plant, the accurate identification of poppy has become the key to anti-drug work. Compared with other spatial data collection methods, unmanned aerial vehicle (UAV) image collection technology can monitor across regions in real time, and its flexibility can improve the efficiency of anti-drug work. However, the UAV art remains short of contrastive assessment of recent deep architectures for poppy objects in different environments UAV images. In addition, foggy weather is often present during the most detectable flowering period for poppies. The existence of fog has a certain impact on the anti-drug work that may be carried out at any time. At present, there is a lack of data samples of poppy pictures in foggy weather, so it is difficult to proceed on related research, to solve the aforementioned problems. This paper compares the detection and statistics of poppies in UAV images by state-of-the-art deep learning-based target detection algorithms in different weather environments. These algorithms include YOLO series and CenterNet. To this end, this paper collects and produces a poppy dataset of UAV images in two different weather environments. Through extensive experiments, the performance of state-of-the-art target detection algorithm to detect poppy under different weather conditions is evaluated.