# EGSDK-Net: Edge-Guided Stepwise Dual Kernel Update Network for Panoptic Segmentation
## 2 Related Work  
Panoptic segmentation methods: One of the primary aims of introducing the panoptic segmentation task is to integrate semantic segmentation and instance segmentation tasks. According to the development progress of this task, it can be divided into two main categories: early panoptic segmentation methods [15, 16, 17, 18] and unified architectures for panoptic segmentation [2, 3, 4, 19]. The early methods either generated instance predictions using bounding box proposals [15, 16] or created panoptic segmentation through alternative design strategies that did not require proposals [17, 18]. In contrast, recent unified architectures for panoptic segmentation directly predict segmentation masks corresponding to their respective classes. The K-Net [2] framework employed a set of learnable kernels for consistent segmentation of instances and semantic categories, proposing a kernel update strategy to resolve the challenges of distinguishing various instances. Mask2 Former [3] constructed a universal architecture for image segmentation, which includes a backbone feature extractor, a pixel decoder, and a Transformer decoder, applying masked attention along with multiple optimization improvements. In addition, some methods focus on real-time panoptic segmentation [5, 10, 11, 12, 13]. [PERSON] et al. [12] tackled panoptic segmentation as a dense classification problem and generated masks for \"stuff\" classes as well as for each instance of \"things\" classes. RT-K-Net [5] is the first unified architecture for real-time panoptic segmentation. It is based on K-Net and comprehensively optimizes its structure, training, and inference processes. YOSO [6] also proposes a real-time panoptic segmentation framework. By utilizing dynamic convolutions and feature pyramid aggregation, YOSO achieves competitive performance while reducing computational overhead, making it suitable for real-time applications. Panoptic SwiftNet [7] focuses on efficient multi-scale feature extraction and boundary-aware learning, making it particularly well-suited for large-scale remote sensing applications. Considering the various shortcomings of the aforementioned methods, we propose the edge-guided stepwise dual kernel update network (EGSDK-Net). Edge-guided methods: Due to edges being considered an additional target attribute, earlier deep learning methods generally did not integrate edge information into the network. Recently, with the growing emphasis on model performance, researchers have begun to recognize that edges can accurately represent the contours of objects in images. Consequently, numerous edge-guided solutions have emerged across various visual tasks [20, 21, 22, 23, 24, 25]. To address the common oversight of edge information in significant object detection tasks for optical remote sensing images, ERPNet [20] introduced an edge-guided recursive localization network, which features edge-aware position attention units as its core module to emphasize prominent objects in these images. MLEFGN [22] introduced a framework that integrates edge detection, edge guidance, and image denoising into an end-to-end CNN model to address the issues of complex scenes and information loss. [PERSON] et al. [24] proposed an edge-guided generative network model to produce semantically consistent outputs from small images, which provide limited information. EGCNet [25] treated the image stitching synthesis phase as an image blending problem and utilized perceptual edges to guide the network with additional geometric priors, enhancing the preservation of structural consistency. Ref. [8] proposed an improved Mask R-CNN algorithm for photovoltaic hot spot segmentation in thermal infrared images. By introducing an edge-guided feature pyramid structure and a spatial attention module, this method significantly improves segmentation accuracy. Ref. [9] addressed the challenge of multimodal fusion for high-resolution remote sensing images.