Back to all available

Lightweight YOLOv8 variants for enhanced real-time surgical tool detection

artificial-intelligence
computer-aided-design
computer-education
computer-vision
data-mining-and-machine-learning

Abstract

Accurate and real-time surgical tool detection is essential in Minimally Invasive Surgery (MIS) to ensure safe and effective Computer-Assisted Interventions (CAI) and Robot-Assisted Surgery (RAS). However, achieving high accuracy under the strict computational limits of embedded surgical platforms remains challenging. This paper introduces a set of enhanced lightweight YOLOv8 variants specifically tailored for surgical tool localization in real-time. The proposed architectures integrate several key innovations: Ghost Convolution for efficient feature extraction, a C2f-Ghost module for compact representation, the SC3T module combining Transformer blocks with Spatial Pyramid Pooling, and attention mechanisms including the Context Augmentation Module (CAM) and Convolutional Block Attention Module (CBAM). Furthermore, the Scylla-IoU (SIoU) loss is employed to improve bounding box regression for elongated instruments. Evaluations on the public m2cai16-tool-locations dataset show that the best variant attains 95.7% [email protected] while reducing parameters and GFLOPs by up to 3× and 1.8×, respectively, compared to the YOLOv8 baseline. These results demonstrate that our design achieves state-of-the-art accuracy with substantially lower complexity, enabling practical deployment in resource-constrained, real-time surgical systems.
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].