STFormer: Cross-Level Feature Fusion in Object Detection

Object detection algorithms can benefit from multi-level features, which encompass both high-level semantic information and low-level location details. However, existing detection methods face numerous challenges in effectively utilizing these multi-level features. Most existing detection techniques utilize simplistic operations such as feature addition or concatenation to fuse multi-level features, thereby failing to effectively suppress redundant information. Consequently, the performance of these algorithms is significantly constrained in complex scenarios. To address these limitations, this paper presents a novel feature extraction network that incorporates joint modeling and multi-dimensional feature fusion. Specifically, the network partitions the features of each level into tiles and employs hybrid self-attention mechanisms to extract these grouped features more comprehensively. Additionally, a hybrid cross-attention-based approach is utilized to regulate the transmission proportion of each grouped feature, facilitating the seamless integration of high-level semantic features obtained from deep encoders and the low-level position details retained by the pipeline. Consequently, the network effectively suppresses noise and enhances performance. Experimental evaluation on the MS COCO dataset demonstrates the effectiveness of the proposed approach, achieving an impressive accuracy of 54.3%. Notably, the algorithm showcases exceptional performance in detecting small-scale targets, surpassing the capabilities of other state-of-the-art technologies.