Academic Journal of Computing & Information Science, 2026, 9(2); doi: 10.25236/AJCIS.2026.090202.
Zhen Liu
School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China
To improve the accuracy of vehicle detection for automotive autonomous driving systems under low-illumination, low-contrast, and dynamic lighting interference conditions at night, this paper introduces a lightweight enhanced model named GCD-YOLO based on the YOLOv12 architecture, focusing on strengthening multi-scale feature extraction and fusion mechanisms. First, to enhance the model’s sensitivity to contours and edges and enable learning of richer image feature representations, a Global Edge Information Transfer (GEIT) network is proposed to refine the backbone network, thereby improving detection accuracy. Second, the bottleneck module (A2C2F) is redesigned by incorporating a Content Aware Mixer (CAMixer) module, which achieves efficient and high-quality image super-resolution reconstruction while facilitating better feature fusion. Finally, the original detection head is replaced with a Dynamic Detection Head (DyHead) to mitigate the negative impact of redundant information generated during feature fusion and to increase inference speed. Experimental results demonstrate that GCD-YOLO achieves an accuracy of 94.7% on the BBD100k dataset, representing a 4.6% improvement over YOLOv12, with a detection frame rate of 59.8 frames per second, thus balancing detection accuracy and speed.
Vehicle Detection, Low-light Environments, Feature Fusion, Yolov12
Zhen Liu. GCD-YOLO: Enhanced Vehicle Detection in Low-Light Conditions via Edge Information Transfer and Dynamic Head. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 2: 11-17. https://doi.org/10.25236/AJCIS.2026.090202.
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