A Hybrid Attention Mechanism and RepGFPN Method for Detecting Wall Cracks in High-Altitude Cleaning Robots

Abstract

Aiming at the problem that cracks with different shapes and scales on the exterior walls of high buildings are difficult to detect, this paper proposed a wall crack detection method for high-altitude cleaning robots by hybridizing the GAM attention mechanism and RepGFPN. First, the GAM attention mechanism was incorporated into the YOLOV5 backbone network to reduce information and amplify global features to improve the accuracy of feature extraction. Then, the neck network incorporated the RepFPN method to improve the descriptive ability of fused multi-scale features and to increase computational efficiency. Public datasets Concrete Crack Images for Classification, Mixed VOC2007, CrackForest-dataset-master, and UCMerced_LandUse were used for experimental validation. The ablation experiment results show that the average accuracy of mAP is improved by 13.5% after introducing the GAM attention mechanism under the yolov5 s original model, while the method in this paper (GR-YOLO) continues to improve by 4.7%. The experimental results show that the average accuracy mAP of the proposed method (GR-YOLO) is 24.0%, 47.1% and 41.0% higher than that of the model yolov5s + involution, yolov5s + p2 + involution and yolov5s + p2 + involution + CBAM, respectively. The method proposed in this article can more effectively improve the accuracy of crack detection and has important application prospects.

Keywords

• Wall defect
• crack detection
• deep learning
• attention mechanism
• special clinic pyramid