Yu, J.; Zhang, J.; Guo, J.; Li, Y.; Wen, S.; Li, R.; Li, L. and Lv, S. (2026) An intelligent pavement crack recognition and automatic repair algorithm based on improved DeepLabv3+ and greedy algorithm. Engineering, Construction and Architectural Management, pp. 1-19. ISSN 0969-9988
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | crack identification; deep learning; greedy algorithm; path optimization; pavement cracks |
| Index terms: | optimization algorithm, ant colony optimization, methodology, integrated solution, repair, efficiency, stability, deep learning |
| Subjects: | innovation studies, artificial intelligence, algorithms, maintenance engineering, performance management, decision-making and optimization, research methods, structural engineering |
| Topics: | Digital Applications, Business Strategy, Research Practice, Engineering Principles, Quality Management |
| Descriptive scope: | 3 CTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here