An intelligent pavement crack recognition and automatic repair algorithm based on improved DeepLabv3+ and greedy algorithm

Purpose To address the growing need for pavement maintenance, this paper presents a two-stage algorithm that integrates intelligent crack identification with automated repair path planning, aiming to boost the intelligent repair efficiency of pavement crack diseases. Design/methodology/approach In the intelligent crack identification phase, a lightweight modified DeepLabv3 + model is used for pixel – level pavement crack segmentation, and operations such as thinning, spur removal and crack segmentation are applied to extract crack skeleton information. In the crack repair path planning phase, the absolute greediness of traditional greedy algorithms is overcome by adding a probability – based crack endpoint selection mechanism. Meanwhile, to boost the greedy algorithm's in – depth optimization ability, three local optimization operators, namely 2 – opt, 3 – opt, and 2 – wopt, are designed to further optimize automated repair paths. Findings To verify the improved greedy algorithm's efficiency, the paper designed experimental scenarios with 10, 20 and 30 cracks each in the repair area. In these scenarios, it compared three algorithms: ant colony optimization (ACO), traditional greedy algorithm (GA) and the proposed improved greedy algorithm (IGA). The comparison focused on optimization ability, efficiency and solution stability. Results showed that the IGA outperformed the other two in optimization and efficiency. Originality/value This paper integrates deep learning with an improved optimization algorithm to present an integrated solution for one - step road crack repair.