This study aims to improve the detection of small-scale microorganisms in complex microscopic environments using advanced deep-learning techniques. Specifically, we target the detection of diplococci in images captured through microscopy of live (dynamic) samples. We propose a method featuring a Localized and Global Context Attention mechanism within a multi-head framework and an innovative Feature Emphasis Adjustment to enhance detection accuracy. Our approach addresses the challenges posed by small microorganisms, which are often difficult to detect due to their small size, intricate backgrounds, and complex structures. By dynamically adjusting the attention scale across different heads, our method captures detailed features at multiple levels, significantly improving the detection and description of small microorganisms. Additionally, we introduce a softmax-based reweighting function that selectively emphasizes essential features for object recognition, reducing noise and irrelevant information. Our model surpasses the accuracy of existing state-of-the-art solutions. Furthermore, we provide a newly curated dataset specifically designed for microorganism detection, featuring a variety of annotated microscopic images for training and evaluation. These contributions advance the understanding of attention mechanisms in deep neural networks and offer practical improvements for applications requiring precise microorganism detection.