Open Access

ARTICLE

The Research on Low-Light Autonomous Driving Object Detection Method

Jianhua Yang^*, Zhiwei Lv, Changling Huo

School of Electronic Information Engineering, Xi’an Technological University, Xi’an, 710021, China

* Corresponding Author: Jianhua Yang. Email:

(This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)

Computers, Materials & Continua 2026, 86(1), 1-18. https://doi.org/10.32604/cmc.2025.068442

Received 29 May 2025; Accepted 05 September 2025; Issue published 10 November 2025

Abstract

Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing, this paper proposes a YOLO-LKSDS automatic driving detection model. Firstly, the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target; then, on the basis of the YOLOv5 model, the Kmeans++ clustering algorithm is introduced to obtain a suitable anchor frame, and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection. Finally, an improved SEAM (Separated and Enhancement Attention Module) attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes. Compared with the original model, the improved YOLO-LKSDS model achieves a 13.3% improvement in accuracy, a 1.7% improvement in mAP, and 240,000 fewer parameters on the BDD100K dataset. In order to validate the generalization of the improved algorithm, we selected the KITTI dataset for experimentation, which shows that YOLOv5’s accuracy improves by 21.1%, recall by 36.6%, and mAP50 by 29.5%, respectively, on the KITTI dataset. The deployment of this paper’s algorithm is verified by an edge computing platform, where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W, demonstrating high real-time capability and energy efficiency.

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