Aiming at the problems of high detection false alarm rate, low detection accuracy, leakage and false detection caused by dense target arrangement, large scale difference and complex background of remote sensing images, a remote sensing image detection algorithm YOLOv8-EP based on YOLOv8n is proposed. Firstly, a feature focus diffusion pyramid network (FFDPN) is constructed to capture multi-scale information through parallel deep convolution, while adding a diffusion mechanism to diffuse the feature information to each detection scale to enhance feature interaction. A lightweight task align dynamic detection head (TADD) is designed to improve the localisation and classification performance of detection through feature sharing and parallel task processing. Then, the SimAM attention mechanism is introduced to capture key information in the image and increase the model sensory field. Finally, the Inner-CIoU loss function is introduced to improve the detrimental effect of low-quality images on the network gradient and accelerate the model convergence. Experimental results on the NWPU VHR-10 dataset and RSOD dataset show that YOLOv8-EP achieves a mAP of 97.6% and 97.9%, respectively, with a 13% decrease in the number of parameters, and improves by 2.2% and 1.5% compared to the YOLOv8n baseline network, which can meet the requirements of industrial deployment and achieve good detection performance overall.