Object detection in remote sensing images has become a vital aspect of the overall object detection domain. To address the problems of missed detection and false detection of small-scale objects in high-resolution remote sensing images with complex backgrounds， a local adaptive feature weighting algorithm is proposed at the detection stage， combined with the YOLOv5s algorithm. By learning the existing label box information， the foreground containing target features is separated from the background， and the local features of the target with key information in the foreground are obtained. The spatial scale and weight of local features of each layer are calculated adaptively. Meanwhile， a global attention mechanism is proposed to enhance the interaction capability of cross-dimensional feature information between channels and spatial dimensions in the backbone， so as to strengthen the correlation between features and compensate for the loss of global information within local features at the detection stage， thereby reducing the rates of missed detection and false detection of targets. Experimental results show that the improved algorithm achieves certain improvements in precision and recall， with a mean average precision reaching 72.33%， representing an increase of 2.66% compared to the traditional YOLOv5s algorithm.