A new target detection method G-YOLO v7 (ghostnet yolo v7) based on GhostNet and attention mechanism with large detection head network structure is proposed to solve the problems of high missed detection rate, low detection success rate and large model volume of traditional unmanned aerial vehicle target detection algorithm. This technology adds a large 160x160 target detection head on the basis of YOLO v7-tiny to improve the small target detection ability, and lightweight processing is performed on the network. The original 20x20 minimum detection head and its convolution structure are deleted, and GhostNet convolution module is added to reduce the number of network parameters and model volume. At the same time, the loss function is modified to WIoU (wise intersection over union), and PCBAM (parallel convolutional block attention module) attention module is added to improve the detection accuracy. The experimental results show that the mAP@0.5 of target detection based on G-YOLO v7 network structure is 42.3%, which is 5.2% higher than that of YOLO v7-tiny, 7.4% higher than that of YOLO v8n. The parameter quantity and model volume of G-YOLO v7 are only 33.9% and 37.9% of YOLO v7-tiny respectively, 64% and 75.6% of YOLO v8n respectively, which can be effectively applied to unmanned aerial vehicle aerial image target detection.