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A discernible correlation emerged between global bridge disasters and bridge deterioration in recent years. To assist bridge inspectors in conducting deterioration identification, this paper presents a Pilgrimage Walk Optimization (PWO) algorithm inspired by Taiwan's unique Matsu bobee custom. The search behavior of the PWO algorithm simulates the gathering of devotees following Matsu's palanquin and their collective movements based on folk belief activities, including divination block casting, pilgrimage, the leisure ceremony, crawling beneath the palanquin, palanquin robbing, and the return palanquin ceremony. Analysis results show that the PWO outperformed contemporary metaheuristic algorithms on various multidimensional benchmark functions and was effectively applied to image segmentation for multiple types of bridge deterioration collected by an unmanned aerial vehicle. In the increasing number of engineering problems requiring edge computing to process big data, the proposed PWO algorithm is a low-cost and robust method with fault tolerance. • Presenting the innovative Pilgrimage Walk Optimization (PWO) algorithm, a revolutionary metaheuristic approach. • The new algorithm is tested on benchmark functions and applied to optimize image segmentation. • PWO algorithm outperforms contemporary metaheuristic algorithms in terms of achieving optimal solutions. • Exploring diverse bridge deterioration patterns auto-segmented using metaheuristic clustering algorithms. • PWO algorithm holds potential applications in AIoT technology and automation within the construction industry. [ABSTRACT FROM AUTHOR]