YOLOv5_CDB: A Global Wind Turbine Detection Framework Integrating CBAM and DBSCAN

Wind energy plays a crucial role in global sustainable development, and accurately estimating the number and spatial distribution of wind turbines is crucial for strategic planning and energy allocation. To address the critical need for wind turbine detection and spatial distribution analysis, this study develops YOLOv5_CDB, an enhanced detection framework based on the YOLOv5 model. The proposed method incorporates two key components: the Convolutional Block Attention Mechanism (CBAM) to improve feature representation and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for spatial density clustering. The method is applied to 2 m resolution World Imagery data. It detects both tubular and lattice wind turbines by analyzing key features, including turbine towers and shadows. The YOLOv5_CDB demonstrates a substantial enhancement in performance when compared with the YOLOv5s. The F1-score shows an increase of 1.39%, and the mean average precision (mAP) exhibits a 1.5% improvement. Meanwhile, the precision (P) and recall (R) values are recorded at 95.97% and 91.18%, respectively. Furthermore, YOLOv5_CDB evinces consistent performance advantages, outperforming state-of-the-art models including YOLOv8s, YOLOv12s, and RT-DETR by 1.84%, 3.98%, and 1.77% in terms of F1-score and by 3.7%, 4.5%, and 3.0% in terms of mAP, respectively. The YOLOv5_CDB model has been demonstrated to show superior performance in the global wind turbine detection domain, thereby providing a foundation for the management of wind farms and the development of sustainable energy.