A target detection algorithm for aerial images based on an improved attention mechanism is suggested to address the issue that the existing object detection network based on horizontal view images has a high false-positive rate and a high miss rate in aerial images. First, a trident channel and spatial attention module that extracts multi-mode and multi-scale characteristic map data of three-branch pooling layers and three-branch dilated convolution layers is added at the output of the Faster R-CNN backbone network so as to compress the data, thereby redistributing the weight of feature channels and spatial pixel regions. Second, a double-head detection mechanism is employed for the classification of the objects and bounding box regression in the aerial image to fully utilize the semantic and spatial location information. The suggested algorithm is further assessed on relevant datasets and contrasted with other object detection algorithms. The results indicate a significant enhancement of the mean average precision of the suggest algorithm, leading to better target detection for unmanned aerial vehicle images in various scenes.