Remote sensing small target images often suffer from issues such as overly dense targets, small target sizes, and difficulty in feature extraction, leading to low detection accuracy for existing object detection algorithms. To address these problems, this paper proposes an SBC-YOLOv8 algorithm for remote sensing small target detection based on YOLOv8 and incorporates the SAHI slicing method. First, the SAHI slicing method is applied to slice the remote sensing small target images, effectively mitigating the problems of excessive target density and small sizes. Second, a Space-to-Depth module is added to the Backbone of YOLOv8 to enhance the network’s feature extraction capability, effectively addressing the challenge of extracting small target features. Then, a BiFPN feature fusion method is employed, and the original P5 layer is replaced with the P2 layer, strengthening the network’s multi-scale feature fusion ability and improving detection accuracy. Finally, the CSP-OmniFusion module is adopted to further address the difficulty of extracting remote sensing small target features. Experimental results show that, compared to the original YOLOv8 algorithm, the SBC-YOLOv8 algorithm with SAHI improvements yields a 23.4% and 18.3% increase in mAP@0.5 on the validation and test sets of the VisDrone2019 dataset, respectively; mAP@0.5∶0.95 increases by 17.4% and 12.4%, respectively. Additionally, on the CARPK and HRSID datasets, mAP@0.5 increases by 1.6% and 1%, and mAP@0.5∶0.95 increases by 6.1% and 2.7%, respectively. Therefore, the proposed algorithm effectively improves the detection performance of remote sensing small target images.