# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 6 Conclusions  
In this work, a vehicle/pedestrian detection algorithm was designed based on YOLOX. In addition, in order to further improve the detection accuracy of the algorithm under low-light conditions, a training strategy based on data domain transfer was proposed. We trained the improved YOLOX with the proposed domain transfer strategy and achieved satisfactory results. The major remarkable features of the proposed approach are:  
(1) The ablation experiments revealed that the improvement in the feature pyramid part of the YOLOX model improved the most, with a 1.8% improvement in mAP. By adding a fusion detection branch at the large-scale feature map in the backbone network and fusing it with the original three smaller-scale feature maps, the detection capability of small, dense targets is effectively enhanced by retaining the original deep image semantic information in order to obtain more shallow image feature information. (2) Introducing the coordinate-based attention mechanism in the YOLOX backbone network can improve the feature extraction capability of the deep model, but the extra computational effort increases the model inference time by 7.7 ms; by simplifying the attention mechanism module from a three-layer structure to a one-layer structure, the inference time can be reduced by 5.3 ms at the cost of a 0.5% decrease in mAP. (3) A model training strategy based on data domain transfer was proposed, in which the nighttime and daytime datasets are domain transferred and then mixed for training by combining low-light enhancement and low-illumination degrading methods. After training  
Figure 7: Comparison of detection results under the night scenario. (**a**) YOLOX; (**b**) improved YOLOX (ours); (**c**) improved YOLOX + Domain Transfer (ours). the improved algorithm with the domain transfer strategy, the detection performance of both nighttime targets is significantly improved, in which the mAP is increased by 2.8%. (4) The improved algorithm, after being trained by the domain transfer strategy, eventually improved the average detection accuracy of nighttime vehicle/pedestrian targets by 5.9% to 82.4%. Our proposed method can effectively improve the target detection accuracy of self-driving vehicles in a nighttime environment and has good implications for other target detection tasks in low-light environments. However, it is still a long way from the highly reliable sensing method needed to guarantee safe nighttime driving. In the future, we will further explore the target detection task in fog conditions and explore the fusion of visual detection with other techniques such as radar and lidar. Guarantor: [PERSON] is responsible for the entirety of the work and the final decision to submit the manuscript. Conceptualization, K.Y. and R.H.; methodology, K.Y., K.L. and T.C. ; software, K.L. and T.C. ; validation, K.Y., K.L. and T.C. ; formal analysis, K.Y. and K.L. ; resources, K.L. and T.C. data curation, K.L. and T.C. ; writing--original draft preparation, K.Y. and K.L. ; writing--review and editing, K.Y., K.L., T.C. and R.H.; visualization, K.L. and T.C. ; supervision, K.Y. and R.H.; project administration, K.Y. and R.H.; funding acquisition, K.Y. and R.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of China, grant number 52002036, partly supported by the Hunan Provincial Natural Science Foundation of China, grant number 2022 JJ30611, the Scientific Research Fund of Hunan Provincial Education Department, grant number 21B0342, the Changsha Science and Technology Major Project, grant number kh2202002, and the Postgraduate Scientific Research Innovation Project of Hunan Province, grant number CX20220903. Not applicable. Data Availability Statement: Data are available in a publicly accessible repository.