A Segmentation Network and an Evaluation Method for Conveyor Belt Damage Detection Based on Improved YOLOv11

Abstract

The belt conveyor is an important continuous transport device in modern industrial production. The conveyor belt, a crucial part of the belt conveyor, is vulnerable to damage since it works for lengthy periods of time at high speeds and large loads. If these damages are not detected and addressed in a timely manner, they may hasten the conveyor belt’s wear and even lead to safety accidents. This paper suggests a conveyor belt damage detection and segmentation network, BDSE-YOLO, based on an enhanced YOLOv11, to address the problems of low detection accuracy, poor real-time performance, and insufficient adaptability to complex backgrounds in the current conveyor belt damage detection methods. First, the YOLOv11 architecture is optimized by introducing the ACmix module in the feature extraction module. A new C2PSA_ACmix module is designed to leverage the self-attention characteristics of the ACmix module, enhancing the network’s capacity to extract both local and global characteristics, thereby improving the performance of damage segmentation and detection, particularly for small or complex damages. Additionally, the iRMB module is added to the backbone network to enhance information flow. This module captures long-range dependencies while maintaining the lightweight nature of the network, enhancing the efficiency and accuracy of segmentation tasks. On this basis, a damage evaluation method based on geometric features and size quantification is proposed. The rupture direction is determined using an ellipse fitting algorithm, while size quantification techniques are employed to accurately analyze the damage morphology and eight quantification indicators are established. Experimental results on a self-made dataset and two public datasets demonstrate that the suggested model attains 96.2%, 81.0% and 92.7% accuracy rates, respectively, outperforming the comparison models and demonstrating high detection accuracy and robustness. The model exhibits strong adaptability in complex industrial environments, and the eight proposed evaluation indicators provide reliable criteria for evaluating rupture propagation trends and the severity of damage. The proposed network and method offer an effective solution for the intelligent detection and evaluation of damage to conveyor belts.