Addressing the challenge of missed detection caused by many small targets and dense arrangement in remote sensing images, this study introduces a small target detection algorithm for remote sensing applications, leveraging a combination of feature synergy and micro-perception strategies. Initially, we propose a refined feature synergistic fusion strategy that optimizes the interaction and integration of features across different scales by intelligently adjusting the parameters of convolution kernels. This strategy facilitates progressive refinement of features from coarse to fine granularity. Building upon this foundation, a micro-perception unit is developed in this paper, incorporating perceptual attention mechanisms with moving inverse convolution to form an advanced detection head. This innovative approach substantially boosts the network's capability to detect very small objects. Furthermore, to augment the training efficiency of the model, we employ MPDIoU and NWD as regression loss functions, mitigating positional bias issues and expediting model convergence. Experimental evaluations on the DOTA1.0 dataset and DOTA1.5 dataset reveal that our algorithm achieves a substantial improvement in mean Average Precision (mAP) by 7.4% and 6.1% over the baseline method, which has obvious advantages over other algorithms. The results underscore the algorithm's efficacy in significantly reducing the incidence of missed detections of small targets within remote sensing imagery.