Context-Aware Feature-Fusion for Co-occurring Object Detection in Autonomous Driving

Researchers have developed Context-Centric Feature Fusion (CCFF), a framework that uses two attention-based modules to improve detection of co-occurring objects in autonomous driving. The system achieved a Category-level Consistency Strategy of 0.973 on Cityscapes and 0.969 on BDD100K, with a 14.1% improvement in small object detection and successful recovery of rare classes like "Train" while adding only 0.2 FPS overhead.

arXiv:2606.12628v1 Announce Type: new Abstract: Object detection in autonomous driving requires precise localization and an inherent understanding of the relational context between co-occurring objects. In extremely complex heterogeneous environments rare classes, small-scale objects, and frequently appearing objects are difficult for standard object detection frameworks to handle. In this paper, we propose a novel framework called Context-Centric Feature Fusion CCFF , which utilizes two attention-based modules, Local Context Fusion Module LCFM uses the RoI-to-RoI self-attention mechanism to resolve spatial interactions, mainly considering small and partially obscured objects, while Global Context Attention Module GCAM converts the co-occurrence of objects priors by pooling top-K RoI features into a global context attention token, avoiding the computational overhead of pixel-level global pooling. This fusion of local and object-centric global features yields contextualized embeddings that enhance classification results and co-occurring objects detection. Our method is evaluated on two datasets, Cityscapes and BDD100K which demonstrate significant improvement on relational consistency, achieving a Category-level Consistency Strategy CCS of 0.973 and 0.969, respectively. Furthermore, our approach produces substantial gains in small object detection AP S: 14.1% and successfully recovers rare classes such as "Train" that are typically lost in large distributions. Our efficiency report shows that the framework processes images in real time with a 0.2 FPS overhead. The code is available at https://github.com/BinayKSingh/CCFF.