# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 4 Conclusions  
To address the challenges of low detection accuracy, high miss rates, and inaccurate target localization in low-light object detection, this paper proposes Dark-YOLO, a low-light object detection algorithm based on dynamic feature extraction. Firstly, the SCINet module was used to enhance the quality of low-light images, effectively restoring image details and enriching the feature representation. Secondly, the CSPFF module was designed to facilitate the effective propagation of both global and local image information. Subsequently, a dynamic feature extraction module (PSM) was introduced, incorporating partial convolution and a parameter-free attention mechanism to flexibly capture critical and effective information from the image. Finally, the D-RAMiT module was added to address the issue of the model focusing exclusively on either local or global features. Experiments conducted on the publicly available ExDark low-light dataset demonstrated that the Dark-YOLO algorithm effectively mitigates issues such as poor detection performance, missed detections, false positives, and inaccurate localization under low-light conditions. The experimental results showed that the proposed model achieved a 3.0% improvement in [EMAIL_ADDRESS] and a 2.5% improvement in [EMAIL_ADDRESS]:0.95 compared to the baseline. The improved model has a size of 5.7 MB, a computational complexity of 7.5 GFLOPS, and contains 2,744,971 parameters. These results provide strong evidence that the proposed model excels at performing object detection tasks in low-light environments, achieving superior detection performance with relatively low computational resource consumption. Additionally, evaluations on the Dark-Face dataset further validate the robustness of Dark-YOLO in low-light face detection tasks, which are critical for nighttime surveillance and security applications. On this dataset, Dark-YOLO achieves an [EMAIL_ADDRESS] of 49.1%, outperforming the baseline YOLOv8n ([EMAIL_ADDRESS] of 46.8%) by 4.9%, and an [EMAIL_ADDRESS]:0.95 of 21.9%, demonstrating its capability to accurately detect faces under challenging illumination conditions with minimal resource overhead.  
While Dark-YOLO significantly improves detection performance on datasets with limited data, such as the ExDark (6:2:2) split, its effectiveness on small-sample datasets remains an area for further enhancement. The model's ability to generalize and perform optimally in scenarios with extremely scarce training data requires additional investigation and optimization.  
Figure 13: (**a**) Labeled face images from the Dark-Face dataset. (**b**) Detection results from the baseline YOLOv8n model after training, showing missed faces and lower confidence scores in low-light conditions. (**c**) Detection results from the Dark-YOLO model after training, demonstrating accurate face detection with high confidence scores even in dimly lit scenarios.  
**Author Contributions:** Conceptualization, Y.L. and S.L.; Methodology, Y.L. and S.L.; Software, Y.L.; Validation, L.Z., H.L. and Z.L.; Formal analysis, Y.L. and S.L.; Data curation, L.Z. and H.L.; Writing-original draft, Y.L.; Writing-review & editing, Y.L.; S.L. and H.L.; Supervision, S.L.; Project administration, S.L.; Funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the sub-project of the Chinese Key Research and Development Program, CARS, specifically the project on micro-mirror scanning excitation and image processing research, Project No.: ZZG0012301.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.