Aiming at the problems of low detection accuracy and slow speed of traditional target detection algorithms, an improved YOLOv4 target detection model is proposed to detect four types of common power equipment and faults in overhead lines, such as poles, transformers, pole tilts, and insulators drop. Instead of the backbone network in the original YOLOv4, MobileNet which is designed for the embedded platform is deployed in this model, making this model lightweight. In order to further reduce the computational complexity and strengthen the learning ability of the convolutional neural network, a deep separable convolution and a CSP structure is introduced in the neck network. This improved model is used to conduct experiments on the overhead line image data set, and the experimental results show that this model can increase the detection speed to 1.68 times of the original model with a equivalent detection accuracy. It can be better applied to embedded devices, and thus achieves the real?time detection of common power equipment and faults in overhead lines by drones.