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

Binay Kumar Singh, Niels Da Vitoria Lobo

Keywords: Object detection, contextual fusion, attention, autonomous driving.

TL;DR: Context-Aware Feature-Fusion improves frequently occurring object detection in autonomous driving by leverage spatial co-occurrence relationships between objects.

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.

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Submission Number: 29