Neighborhood Attention-based Transformer Line Segment Detector with Edge Computing

Line segment detection is increasingly being widely applied in visual tasks. Traditional methods for line segment detection are known for their speed and accuracy, but they lack robustness in handling noisy images. CNN-based line segment detection methods achieve impressive detection results by extracting more complex features. Nowadays, with the development of edge computing, these methods are being optimized for edge devices to enable on-device processing without the need for extensive cloud resources. However, current CNN-based methods fail to effectively utilize the global features of an image, and they usually require the stacking of a large number of convolutional layers to achieve the best performance, resulting in high computation costs. Transformer-based line segment detection methods have gradually emerged as promising solutions for edge computing applications. They leverage the power of distributed attention mechanisms to capture both global and local features, making them suitable for edge devices with limited computational resources. In this paper, we introduce the concept of edge-enhanced Transformer-based line segment detection, specifically the Neighborhood Attention-Based Transformer (NBAT). By incorporating neighborhood attention into the Transformer architecture, NBAT efficiently processes image data on the edge, focusing on local regions of interest. This localized approach not only ensures real-time responsiveness but also maintains accuracy in detecting line segments. Extensive experiments demonstrate that the proposed NBAT achieves higher detection accuracy.