<p>YOLOv8 excels in generic horizontal bounding box detection but faces significant challenges when detecting oriented objects in remote sensing imagery. Targets in remote sensing images often exhibit large-scale variations, irregular shapes, and arbitrary orientations, which pose substantial difficulties for oriented detection. Additionally, inherent issues such as inconsistency in loss measurement and angle discontinuity further complicate oriented detection. Existing methods typically address only singular aspects of these challenges. In this paper, we propose an advanced adaptive detector based on YOLOv8, named AdaR-YOLOv8, which comprises three innovative strategies.For feature extraction, we designed the C2f-DCN module based on deformable convolutions, allowing the convolutions to dynamically adjust to target shapes. For feature fusion, we introduced the Multi-Scale Sequential Feature Fusion (MSFF) mechanism, which utilizes a three-dimensional (3D) convolution to effectively merge features across scales, thereby enhancing the detection accuracy of smaller targets. For the loss function, we incorporated the KFIoU loss function, similar to SkewIoU, to address angle regression issues. Experimental results on the DOTA and HRSC2016 datasets demonstrate that AdaR-YOLOv8 significantly improves detection accuracy, establishing a new benchmark in oriented object detection for remote sensing imagery.</p>