Detection of Greenhouse and Typical Rural Buildings with Efficient Weighted YOLOv8 in Hebei Province, China

The large-scale detection of greenhouses and rural buildings is important for natural resource surveys and farmland protection. However, in rural and mountainous areas, the resolution and accessibility of remote sensing satellite images from a single source are poor, making it difficult to detect greenhouses and rural buildings effectively and automatically. In this paper, a wide-area greenhouse and rural building (GH-RB) detection dataset is constructed as a benchmark by using high-resolution remote sensing images of Hebei Province, China, collected from the image platform. Then, Efficient Weighted YOLOv8 (EW-YOLOv8) is proposed by using the dataset with unbalanced and small samples of greenhouse and rural buildings, in which the improvement measures are introduced. These include the following: (1) replacing the traditional up-sampler with DySample in the up-sampling part of the neck of the model to recover the lost details after multiple down-sampling operations; (2) replacing the calculation loss function with NWD loss to compensate for the sensitivity of the IoU to the position deviation of small objects; and (3) introducing a weight function named Slide to resolve the data imbalance between easy and difficult samples. The experimental results show that the proposed method can achieve excellent object detection performance on the RSOD dataset compared with state-of-the-art methods, proving the effectiveness of the proposed EW-YOLOv8. The results on the constructed GH-RB dataset show that the proposed method can detect greenhouse and rural buildings quickly and accurately, which could help improve the efficiency of investigating farmland usage and performing natural resource surveys.