In order to solve the problems of background noise interference, variable scale and low detection accuracy caused by small scale defects in insulator defect detection, an insulator defect detection algorithm based on guided attention and scale perception (GASPNet) is proposed. First, a guided attention module (GAM) is constructed on the backbone network to guide the attention of deep features by using shallow features that have a stronger ability to express small targets, and combining channel and space bidirectional attention to reduce the interference of background noise. Second, in the neck network, a feature enhanced fusion network (FEFN) is proposed to enhance the effective fusion of semantic information and local information by cross-fusing different levels of feature information. Finally, the EIoU loss function is used to define the penalty term by combining the vector angle and position information, which improves the regression accuracy of the detection box and achieves accurate detection of small scale targets. The experimental results show that the mean average precision (mAP@0.5) of GASPNet on the insulator defect detection dataset reaches 94.8%, and the detection speed is 95.3 frame/s, which is significantly better than other detection algorithms. At the same time, the embedded experiments verify that GASPNet still has efficient real-time detection performance under the condition of limited computing resources, which is suitable for practical application scenarios.