Aiming at the problems of low efficiency and poor accuracy of glue mark detection caused by poor lighting conditions, a multi-threshold segmentation method of airport runway glue marks based on improved sparrow search algorithm was proposed. Firstly, the lens imaging reverse learning is used to improve the diversity of the initialized population, and then the optimized performance level and adaptive factor are introduced to improve the individual quality and search ability of the discoverer. Secondly, the firefly algorithm is introduced to assist the traditional sparrow search algorithm to jump out of the local optimum. Finally, use the improved sparrow algorithm to optimize the Tsallis relative entropy metric function to achieve automatic and accurate segmentation of glue traces. The experimental results show that the detection accuracy of this method is much higher than that of the traditional algorithm, its FSIM values are all greater than 0.8, and the SSIM values are close to 1, and it shows a good segmentation effect in the case of poor lighting conditions and the mixture of pavement, marker lines and glue marks.