There is a problem that small object detection of insulators defects is difficult because of complex scenes. Thus this paper proposes a detection model of insulator defects based on dynamic snake convolution and space-to-depth convolution. First, the algorithm imports dynamic snake convolution to shape the feature extraction module that conforms to the characteristics of insulators, improving the ability of feature extraction for insulators and their defects. Next, this algorithm takes the “space-to-depth” convolution to reduce the features missing during the fusion. Finally, the algorithm is pruned to decrease redundancy in order to further simplify the complexity of model. With experiments on an insulator defects dataset, the recognition rates of broken, pollution-flashover and self-exposing insulators are increased by 5.7%, 2.4% and 0.8% respectively. In addition, the detection rate of insulators is raised by 0.5% contrasted to baseline. Meanwhile, mean average precision is increased by 2.3% and model size is diminished by 50.07%. The results of experiments show that the algorithm can not only promote detection accuracy of a small object insulator defect, but also it can sink model size, which means that the algorithm possesses a certain reference and application value for a survey of insulator defect detection.