Research on road crack detection algorithm based on YOLO-SW


Abstract

Efficient and highly accurate road crack detection algorithms are particularly important in road inspection systems. However, some of their limitations have gradually come to the fore as the target detection aspect has become more in-depth. Existing road target detection algorithms face the difficulty of capturing long-range dependencies, resulting in limited feature expressiveness and high leakage rates in small target detection scenarios (e.g., road fine crack identification). Therefore, in this paper, we propose an improved model YOLO-SW based on YOLO11n. Firstly, based on the structure of the C2f module, we introduce the self-developed SP module (responsible for the multiscale feature aggregation and attention mechanism), and propose the SP_C2f module, which enhances the ability of small-target detection. the method replaces the C3k2 of YOLO11n with the C3k2 of YOLO11n to enhance the feature expression ability by combining the multiscale feature aggregation and attention The method replaces C3k2 of YOLO11n with the SP_C2f module that enhances the feature expression ability by combining multi-scale feature aggregation and attention mechanism, which effectively improves the feature expression accuracy of small targets. At the same time, the CGAFusion module is added to enhance the feature expression ability by combining spatial attention, channel attention, and pixel attention. Experiments on the pavement crack detection Computer Vision Project dataset show that the mean average accuracy of the improved YOLO-SW model ([email protected]) reaches 58.2%, which is an improvement of 8.7 percentage points over the baseline model YOLO11n. The experimental results validate the significant advantages of YOLO-SW for crack detection in complex road scenarios.
Ask to review this manuscript

Notes for potential reviewers

  • Volunteering is not a guarantee that you will be asked to review. There are many reasons: reviewers must be qualified, there should be no conflicts of interest, a minimum of two reviewers have already accepted an invitation, etc.
  • This is NOT OPEN peer review. The review is single-blind, and all recommendations are sent privately to the Academic Editor handling the manuscript. All reviews are published and reviewers can choose to sign their reviews.
  • What happens after volunteering? It may be a few days before you receive an invitation to review with further instructions. You will need to accept the invitation to then become an official referee for the manuscript. If you do not receive an invitation it is for one of many possible reasons as noted above.

  • PeerJ Computer Science does not judge submissions based on subjective measures such as novelty, impact or degree of advance. Effectively, reviewers are asked to comment on whether or not the submission is scientifically and technically sound and therefore deserves to join the scientific literature. Our Peer Review criteria can be found on the "Editorial Criteria" page - reviewers are specifically asked to comment on 3 broad areas: "Basic Reporting", "Experimental Design" and "Validity of the Findings".
  • Reviewers are expected to comment in a timely, professional, and constructive manner.
  • Until the article is published, reviewers must regard all information relating to the submission as strictly confidential.
  • When submitting a review, reviewers are given the option to "sign" their review (i.e. to associate their name with their comments). Otherwise, all review comments remain anonymous.
  • All reviews of published articles are published. This includes manuscript files, peer review comments, author rebuttals and revised materials.
  • Each time a decision is made by the Academic Editor, each reviewer will receive a copy of the Decision Letter (which will include the comments of all reviewers).

If you have any questions about submitting your review, please email us at [email protected].