Lightweight design and deployment of object detection model for low-light scenes

Kangjian Sun; Ju Huo; Xiaona Jiang; Yichao Gao; Chen Cai

doi:10.1117/12.3055833

9 January 2025 Lightweight design and deployment of object detection model for low-light scenes

Kangjian Sun, Ju Huo, Xiaona Jiang, Yichao Gao, Chen Cai

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 1348620 (2025) https://doi.org/10.1117/12.3055833
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based night time autonomous driving.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kangjian Sun, Ju Huo, Xiaona Jiang, Yichao Gao, and Chen Cai "Lightweight design and deployment of object detection model for low-light scenes", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 1348620 (9 January 2025); https://doi.org/10.1117/12.3055833

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