基于改进麻雀算法的机场跑道胶痕检测方法

Method for detecting glue mark of airport runway based on FASSA

针对光照条件差导致的胶痕检测效率低、精度差等问题,提出了一种基于改进麻雀搜索算法的机场跑道胶痕多阈值分割方法。首先利用透镜成像反向学习提高初始化种群的多样性,然后引入优化性能级别和自适应因子,提高发现者个体质量和搜索能力,其次引入萤火虫算法,协助传统麻雀搜索算法跳出局部最优,最后采用改进麻雀算法优化Tsallis相对熵度量函数实现胶痕自动、精准分割。实验结果表明,该方法的胶痕检测精度远高于传统算法,其FSIM值均大于0.8,SSIM值接近于1,并且在光照条件差和道面、标志线与胶痕混叠的情况下表现出了良好的分割效果。

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.