The accuracy of the diagnosis of degraded insulators is improved to accurately identify low-zero-value insulators in the power grid. A pair of insulator infrared images and a gray wolf optimizer (GWO) optimized binary support vector machine (SVM) classifier is proposed. Low-zero insulators are detected automatically. First, the infrared image of the insulator is enhanced; then, the infrared image is segmented using the Ostu algorithm; and the obtained binary image is subjected to tilt angle correction and cutting to extract the effective region of the insulator string. Finally, the image features are applied to the classification and recognition of vector machines. The experimental results show that the GWO-SVM can identify the low-zero insulator more accurately and effectively than the commonly used grid search (GS) and particle swarm optimization (PSO). Its rate is higher.