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

• URL: http://arxiv.org/abs/2606.12628v1
• Date: Wed, 10 Jun 2026 19:33:16 GMT
• Title: Context-Aware Feature-Fusion for Co-occurring Object Detection in Autonomous Driving
• Abstract summary: We propose a novel framework called Context-Centric Feature Fusion (CCFF)<n>CCFF uses two attention-based modules, Local Context Fusion Module (LCFM) and Global Context Attention Module (GCAM)<n>This fusion of local and object-centric global features yields contextualized embeddings that enhance classification results and co-occurring objects detection.
• Score: 1.2533121077298057
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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