# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 2 Related Work
### Deep Learning-Based Segmentation  
Dense prediction in deep learning-based object recognition systems can be broadly categorized into object detection tasks, which identify the spatial location of target objects [20], and object segmentation tasks [21], which further predict pixel-level masks for the detected objects. In particular, image segmentation tasks are divided into semantic segmentation [21], which performs pixel-level classification by predicting the presence of object categories in both foreground and background and instance segmentation [22], which predicts separate segmentation masks for each individual object instance. A typical semantic segmentation process consists of an encoder that extracts pixel-level visual representations and a decoder that reconstructs the prediction masks to match the original image resolution. SegFormer [8], in particular, introduces a hierarchical transformer-based encoder designed to capture both coarse and fine features, along with a lightweight All-MLP decoder that fuses multi-level features for accurate mask prediction. DeepLabv3+ [9] utilizes Atrous Spatial Pyramid Pooling (ASPP) to effectively capture objects at multiple scales and introduces a decoder module that enhances the reconstruction of fine object boundaries. Instance segmentation tasks are typically implemented by attaching a mask-specific decoder to the instance representations predicted by a detection agent. YOLOv7 [10] is an advanced version of the original one-stage detection pipeline, YOLO [20], with multiple architectural improvements. By incorporating a dedicated segmentation mask decoder, it enables segmentation prediction while preserving the fast inference speed characteristic of YOLO-based models. Mask2 Former [7] proposes a unified architecture for any image segmentation task--including semantic, instance, and panoptic segmentation--by introducing masked attention for cross-attention between predicted mask regions and a transformer decoder that efficiently leverages high-resolution features from a pixel decoder.  
Recent advances in transformer-based segmentation models and domain-specific detectors have led to the development of increasingly powerful dense prediction techniques. For instance, OneFormer [23] introduces a multi-task unified transformer architecture that utilizes task-specific conditional queries, achieving superior performance across semantic, instance, and panoptic segmentation tasks compared to individually trained models such as Mask2 Former. In the field of pavement crack analysis, [PERSON] et al. [24] proposed the \"Crack Transformer\", which leverages a Swin Transformer encoder to significantly improve pixel-level crack segmentation under challenging conditions such as shadows and complex crack structures, outperforming traditional CNN-based methods like DeepLabv3+. These recent studies highlight the continuous progress in dense prediction--from general-purpose segmentation transformers to task-specific crack detection networks--and underscore the importance of scalable and representative datasets to support further advancement.