Regional Contextual Information Modeling for Small Object Detection on Highways

Small object detection based on highways is still a highly challenging research topic due to the limitation of the number of pixels belonging to an object. Traditional small object detection methods lack the exploration of contextual information since the features of small objects do not have sufficient discriminative information, leading to poor performance. In our research, we present a straightforward yet powerful approach that can be seamlessly integrated as a plug-and-play component, to use contextual information as clues to enrich small object features to improve detection performance. In particular, we design a novel region context module that utilizes the self-attention mechanism to capture the context information in a regional manner. In order to encourage cross-regional interaction of context information, we address a shifted-region partitioning method to offset the features circularly. Additionally, we introduce a channel selection module, which enables the network to adaptively select the suitable channel to enhance the ability to classify. Comprehensive experiments demonstrate that our method is effective, e.g., our method achieves mean average precision (mAP), AP0.5, AP0.75, and APs of 56.8%, 89.6%, 61.9%, and 49.6% on the small object detection on highways (HSOD) dataset, respectively. Besides, extensive ablation studies and visualizations are provided for a better understanding of our method.