Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These structures allow the model to better recognize the semantic and edge information in the images. Our model is trained from scratch on the BIPED dataset without any pre-trained weights and achieves promising results. Moreover, it generalizes well to other datasets without fine-tuning.

Binocular vision is a core module in humanoid robots, and stereo matching is one of the key challenges in binocular vision, relying on template matching techniques and mathematical optimization methods to achieve precise image matching. However, occlusion significantly affects matching accuracy and robustness in practical applications. To address this issue, we propose a novel hybrid matching strategy. This method does not require network training and has high computational efficiency, effectively addressing occlusion issues. First, we propose the Inverse Template Matching Mathematical Method (ITM), which is based on optimization theory. This method generates multiple new templates from the image to be matched using mathematical segmentation techniques and then matches them with the original template through an inverse optimization process, thereby effectively improving matching accuracy under mild occlusion conditions. Second, we propose the Iterative Matching Mathematical Method (IMM), which repeatedly executes ITM combined with optimization strategies to continuously refine the size of matching templates, thereby further improving matching accuracy under complex occlusion conditions. Concurrently, we adopt a local region selection strategy to selectively target areas related to occlusion regions for inverse optimization matching, significantly enhancing matching efficiency. Experimental results show that under severe occlusion conditions, the proposed method achieves a 93% improvement in accuracy compared to traditional template matching methods and a 37% improvement compared to methods based on convolutional neural networks (CNNs), reaching the current state of the art in the field. Our method introduces a reverse optimization paradigm into the field of template matching and provides an innovative mathematical solution to address occlusion issues.

Efficient coordination among software modules is essential for biomimetic robotic systems, much like the interaction among organs in a biological organism. However, implementing inter-process or inter-module communication in autonomous systems remains a complex and time-consuming task, particularly for new researchers. Simplifying inter-module communication is the central focus of this study. To address this challenge, we propose the DigitalTwinPort framework, a novel communication abstraction inspired by the port-based connectivity of embedded hardware systems. Unlike middleware-dependent solutions such as ROS, the proposed framework provides a lightweight, object-oriented structure that enables unified and scalable communication between software modules and networked devices. The concept is implemented in C++ and validated through an autonomous surface vehicle (ASV) developed for the RobotX Challenge. Results demonstrate that the DigitalTwinPort simplifies the development of distributed systems, reduces configuration overhead, and enhances synchronization between digital and physical components. This work lays the foundation for future digital twin architectures in embodied Internet systems, where software and hardware can interact seamlessly through standardized digital ports.