In order to meet the application requirements on unmanned aerial vehicle (UAV) for daily inspection of insulators in overhead grid transmission lines, a lightweight GhostNet-YOLOV4 model is proposed to identify the insulators objects and detect their self-explosion defect meanwhile. In the proposed model, the lightweight GhostNet with full convolutional attention module (C-SE) model is embedded into the YOLOV4 as backbone feature extraction network, and the ordinary convolution and Mish activation function of enhanced feature extraction network in GhostNet-YOLOV4 are replaced by the depth separable convolution and ReLU6 activation function respectively. Meanwhile, the combination methods of data augmentation, label smoothing, K-Means algorithm, adaptive cosine annealing learning rate, and Focal Loss function are adopted to optimize the model. Finally, the class activation mapping (CAM) is used to visually analyze for the identification process of the proposed model. The example analysis results show that the object recognition accuracy mAP of the proposed model is 99.50% for insulator objects and self-explosion defect, and the image processing speed FPS is 2.53 frames/s. The proposed model meets the requirements of lightweight, detect accuracy and speed for UAV application.