## 1 Introduction
Reference [16] proposed a crack detection method based on frequency-domain images and one-dimensional convolutional neural networks, which is based on sliding window extraction of images, classification of cracks in a single image within the window and then final stitching of the output image, but this method has a slow detection speed of 5-7 s per image. Reference [17] designed a crack detection method based on a semantic segmentation algorithm, which enables pixel-level detection of cracked regions. Their method performs pixel classification of the full image, which loses background information of images, and it does not have real-time detection capabilities. References [18; 19] proposed crack detection methods based on you only look once (YOLO). Their method has some improvements to the algorithms and is able to label cracks using a bounding box in real-time. The introduction of a transformer [20] into YOLOv5 can improve the performance of the model, however,  
Figure 1: A remotely operated vehicle (ROV). the transformer based on the self-attention mechanism is computationally intensive and its introduction into the model will increase the size of the model, increase the model inference time and is not cost-effective. Reference [21] proposed crack detection methods based on object detection and semantic segmentation, respectively. Using the dense annotation method for labelling leads to dense target boxes in the predicted result image, which affects the presentation of cracked regions in the image. The semantic segmentation-based crack detection method is able to detect cracked regions at the pixel level, but classifying the full image pixels leads to the loss of image background information. Since cracks usually have different degrees of cracking and random shapes, and many cracks have small degrees of cracking and long crack trajectories, simply using a bounding box to frame the cracks has a weak detection effect, which is not enough to display the size and track of the cracks. The instance segmentation algorithm combines the features of both object detection and semantic segmentation and is able to box out objects and classify object class at the pixel level, which is more suitable for the surface crack detection task. Reference [22] proposed a crack detection method based on Mask R-CNN [23], but the Mask R-CNN model is complex, computationally intensive and does not have the capability of real-time detection on low performance devices. To address the above issues, this study proposes a crack detection method based on an improved instance segmentation model: LCA-YOLOv8-seg. With high accuracy, low computation and small size, our model is friendly to hardware devices with low performance and our model facilitates further deployment to mobile devices. Our model uses a lighter LCANet backbone, which is based on a depthwise separable convolution and efficient channel attention (ECA) [24] mechanism, with the advantages of light weight and high accuracy. In addition, this study optimises the head structure of YOLOv8-seg by adopting a new module, ProtoC1, which reduces the computational cost of the model. It does not affect the accuracy of the mask. Our model shows a slight decrease in the \(mAP_{0.5}\) metric compared to the baseline model YOLOv8n-seg, while the parameters, weights and GFLOPs of our model all decrease substantially. At the same time, to further improve the robustness of the model and reduce training costs, transfer learning technology is introduced. The contributions of this study can be summarised as follows:  
* A crack detection method based on an improved one-stage instance segmentation model LCA-YOLOv8n-seg is proposed. Our method is able to frame cracks and depict crack regions at the pixel level. Our method is real-time, highly accurate, small in volume and friendly to low performance devices. * A new backbone network LCANet and a novel ProtoC1 module are proposed, which reduces the model volume drastically and has high detection accuracy.