在多尺度物件偵測的應用情景中，由於特徵金字塔網路 (FPN) 本身的尺度離散特性，往往導致尺度斷層 (scale truncations) 以及特徵傳遞過程中的資訊衰減 (information attenuation)，使得偵測效能面臨許多挑戰。儘管近期的方法試圖透過複雜的特徵聚合機制或從層級結構上的密集堆疊來緩解這些問題，但它們往往會帶來高昂的運算成本，或是忽略了跨尺度特徵表示的連續性。為了解決這個問題，我們提出了基於輕量化設計建構的局部-全域整合網路 (Local-Global Integration Network, LoGIN)，有別於現有的方法，LoGIN 從特徵表徵連續性 (representational continuity) 的視角來解決尺度斷層問題。它透過更全面的特徵融合方法去擴展每個離散層級的有效尺度覆蓋範圍，來消除位於尺度邊界處物件的模糊性；同時利用全域上下文資訊來強化語義一致性，有效抑制背景雜訊。在 MS-COCO 資料集上的實驗結果顯示，LoGIN 在輕量級偵測器中展現了極具競爭力的效能，為多尺度物件偵測的實際應用提供了一個更穩健的解決方案。;Efficient multi-scale object detection faces significant challenges due to the discrete nature of Feature Pyramid Networks (FPNs), which often results in scale truncations and information attenuation during transmission. While recent approaches have attempted to mitigate these issues via complex aggregation mechanisms or dense layer stacking, they often incur high computational overhead or overlook the continuity of feature representation across scales. To address this, we propose the Local-Global Integration Network (LoGIN), constructed from a lightweight design. Distinct from existing methods, LoGIN tackles the scale gap problem through a perspective of representational continuity. It expands the effective scale coverage of each discrete level to resolve ambiguities for objects at scale boundaries, while simultaneously leveraging global context to enforce semantic consistency and effectively suppress background noise. Experimental results on MS-COCO demonstrate that LoGIN achieves competitive performance among lightweight detectors, providing a robust solution for practical applications.
