LRODNet: a framework for 3D object detection of railway tracks in low-light environments

Abstract

Accurate detection of railway tracks at various scales in low-light conditions is a critical challenge for ensuring the autonomous and safe operation of rail trains. To address this, this paper proposes LRODNet, a framework for 3D object detection of railway tracks under low-light environments. Specifically, we design an image feature extraction module that efficiently extracts image features through a lightweight convolutional block and a feature adaptive enhancement module. To tackle issues such as low contrast and significant noise in low-light images, the feature adaptive enhancement module is developed to effectively enhance detail representation in key regions while suppressing background interference. Addressing the difficulty of cross-modal matching between image features and LiDAR features, we propose an attention based multi-stage fusion module, which achieves adaptive feature alignment and deep fusion through attention mechanisms. Experiments demonstrate that LRODNet exhibits outstanding performance in detecting railway tracks under low-light conditions, providing an efficient and reliable solution for environmental perception of railway tracks in complex lighting environments.