A YOLOv5-based target detection method using high-resolution remote sensing images

High-resolution remote sensing images contain rich data and information, which reduce the difference between the target and the background, resulting in substandard detection accuracy and reduced target detection performance. Based on the deep learning algorithm You Only Look Once (YOLO), this study designed a lightweight network model GC-YOLOv5 by combining end-to-end coordinate attention (CA) and the lightweight network module GhostConv. The CA was employed to encode channels along the horizontal and vertical directions, enabling the attention mechanism module to simultaneously capture remote spatial interactions with precise location information and helping the network locate targets of interest more accurately. The original ordinary convolutional module convolutional-batchnormal-SiLu (CBS) was replaced by the GhostConv module, reducing the number of parameters in the feature channel fusion process and the size of the optimal model. Experiments were conducted on the GC-YOLOv5 using the publicly available NWPU-VHR-10 dataset, with the robustness of the model verified on the RSOD dataset. The results show that GC-YOLOv5 yielded a detection accuracy of 96.5% on the NWPU-VHR-10 dataset, with a recall rate of 96.4% and mAP of 97.7%. Moreover, GC-YOLOv5 achieved satisfactory results on the RSOD dataset.