HighlightNet: Learning Highlight-Guided Attention Network for Nighttime Vehicle Detection

Abstract

Vehicle detection at night is a crucial task in Intelligent Transportation Systems. Due to the complex lighting environment, vehicle detection at night remains a challenging task. Headlights and taillights are essential cues to identify vehicles at night. However, existing methods struggle to effectively utilize the light information of the vehicle. This paper proposes a novel highlight-guided framework to identify vehicles, named HighlightNet, by utilizing both the illumination data from the vehicle lights and the reflective properties of vehicles. The framework combines vehicle detection and highlight area recognition via dual-branch joint learning. To ensure that both branches focus on the highlighted regions, Feature Similarity Awareness Attention (FSAA) is introduced to capture the common attention regions of different branches. Highlight Region Perception (HRP) is proposed to exclude streetlights and other reflective illuminations from the FSAA output, which generates a mask map capable of differentiating the foreground from the background of highlighted areas. It improves the allocation of feature weights and adaptively modifies the distribution within the dual-branch configuration. Furthermore, to address the severe pixel imbalance between the highlighted area and the background, Adaptive Spatial Balance (ASB) loss is introduced to allocate the attention towards prospective vehicle regions while diminishing the emphasis on background regions. Extensive experiments conducted on the BDD100K-Night dataset and a newly acquired dataset specifically designed for nighttime surveillance, called the NightVehicle dataset, demonstrate that HighlightNet outperforms the state-of-the-art methods for nighttime vehicle detection.