Enhancing precision and efficiency: small object segmentation in high-resolution images using multi-task networks

Semantic segmentation is a typical task in computer vision. To address the issue of poor performance for small objects in high-resolution images by existing semantic segmentation networks, a method based on a multi-task network is proposed in this paper. This approach jointly learns relevant tasks, enhancing the accuracy of small object segmentation. The methodology of upsampling in semantic segmentation has been refined through the amalgamation of sub-pixel convolution and conventional convolution techniques, ensuring the meticulous preservation of intricate details. Additionally, a sophisticated post-processing module has been introduced, effectively enhancing the accuracy of small object edge segmentation. The empirical results highlight the effectiveness of this approach, significantly enhancing the segmentation quality of small objects in high-resolution images, while simultaneously ensuring real-time computational efficiency.