CDC-YOLOFusion: Leveraging Cross-Scale Dynamic Convolution Fusion for Visible-Infrared Object Detection

Feature-level fusion methods have demonstrated superior performance for visible-infrared object detection due to the deep exploration of visible and infrared features. However, most existing feature-level fusion methods utilize multiple convolutional layers with fixed parameters to extract bimodal features, leading to low adaptivity to diverse data distributions. This paper proposes a Cross-scale Dynamic Convolution-driven YOLO Fusion (CDC-YOLOFusion) network, which introduces a novel Cross-scale Dynamic Convolution Fusion (CDCF) module to adaptively extract and fuse bimodal features concerning on data distribution. Technically, CDC-YOLOFusion first designs a novel data augmentation strategy “Cross-modal Data Swapping” (CDS) to exchange local regions between visible and infrared images, effectively capturing cross-modal correlations within local regions. Building on this, the proposed CDCF utilizes cross-scale enhanced features to assist dynamic convolution prediction by introducing a disparity attention mask, which emphasizes the extraction of disparate features between two modalities. Our CDCF is effectively guided by a novel cross-modal kernel interaction loss, aiming the learned kernels to simultaneously focus on common salient features and unique features of each modality for comprehensive feature generation. Extensive experiments on three representative detection datasets demonstrate that CDCF can be easily plugined into the existing pipelines, obtaining consistent performance improvements. Moreover, our approach yields SOTA performance with about 2% to 3% mAP improvements as compared to the state-of-the-art methods.