<p>Recent advancements in deep learning have significantly improved object detection performance, yet scale variation remains a formidable challenge. Classical methods, such as Feature Pyramid Network (FPN), often suffer from semantic dilution and inadequate spatial information transmission. To tackle these issues, we propose a novel framework called Multi-view and Spatial- correlation Interaction (MSI) incorporating three key modules: Multi-head Mixed Fusion (MMF), Spatial-Correlation Propagation (SCP), and Scale-Aware Aggregation (SAA). The MMF module enhances high-level semantic features through deep exploration and a refined learning process. The SCP module utilizes high-resolution features to ensure effective spatial information acquisition across different hierarchical levels, thereby improving the spatial positional accuracy of multi-level features. The SAA module overcomes the constraints of traditional pyramid architectures by facilitating both local and global feature aggregation, thereby enhancing the multi-scale representation capability. Extensive experiments on the COCO dataset demonstrate the effectiveness, superiority, and general applicability of our approach. The proposed method significantly improves the utilization of multi-level features and the accuracy of object localization tasks, providing a robust solution to the scale variation problem in object detection.</p>