CIE-Det: a low-light object detector via cascaded illumination enhancement and dynamic semantic alignment

Abstract

Object detection in low-light environments is severely degraded by photon starvation, leading to feature concealment, noise amplification during feature fusion, and motion blur caused by long exposure. To address the limitations of the conventional “enhancement-then-detection” pipeline, this paper proposes CIE-Det, an end-to-end lightweight object detection framework that jointly optimizes illumination enhancement and feature learning. A lightweight cascaded illumination enhancement module (CIE module) with 0.0017 M parameters is first embedded at the network input to restore image contrast via downsampled illumination estimation and cascaded nonlinear curves with a dynamic gating mechanism. To alleviate cross-layer semantic inconsistency, a dynamic semantic alignment fusion operator is introduced to replace static feature concatenation with learnable adaptive weighting, enabling effective noise suppression and multiscale feature alignment. Furthermore, an orthogonal asymmetric large-kernel detection head (OAK-Detect) is designed using separable 1×5 and 5×1 convolutions to enhance receptive fields and improve robustness to motion blur. Extensive experiments on the ExDark dataset demonstrate that CIE-Det achieves 73.1% mAP@0.5 (mean average precision at an intersection over union threshold of 0.5), outperforming YOLOv11s by 2.5%, while reducing model complexity to 9.12 M parameters. The proposed method achieves a favorable trade-off between detection accuracy and efficiency in low-light scenarios.