In order to solve the real-time aerial object detection requirements under the condition of limited hardware platform resources, we propose a lightweight aerial object detection method for mobile devices/edge computing based on the improved YOLOv5. Firstly, we build a feature extraction network based on MobileNetv3, and design a channel attention enhancement structure MNtECA to improve the feature extraction ability. Then, we add 1×1 convolution to the depthwise separable convolutions layer to reduce the parameters of convolution structure while improving the fitting ability of the network. Finally, we force iterative channel-level pruning to the detection network to achieve model compression and acceleration. We conduct the training and testing experiments on the DIOR dataset, and perform inference verification on the embedded platform (NVIDIA Jetson Xavier NX). The results prove that the proposed lightweight model reduces the number of parameters and the amount of calculation greatly with high accuracy, realizing the real-time aerial object detection of mobile devices/edge computing.