# Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes
## 5 Conclusions  
This paper proposed a road indicator object detection algorithm adapted to the vehicle inspection environment, based on the YOLOv8 benchmark model for improvement and refinement, with the original Backbone unchanged. In the four key nodes, in the Neck, there are a large number of uses of the four kinds of attention mechanisms; first of all, the attention mechanisms were used individually, and it was found that all can bring some optimization to model performance. Therefore, the four attention mechanism models were placed in different positions according to their different functions, playing to the advantages of each attention mechanism, and improving the acquisition ability of the features in multiple  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mAP50** & **mAP50:95** \\ \hline SSD & 72.6 & 30.3 \\ Faster-RCNN & 82.0 & 37.8 \\ CenterNet & 79.5 & 35.8 \\ YOLOv3-tiny & 80.2 & 38.1 \\ YOLOv5s & 83.4 & 38.7 \\ YOLOv7 & 86.2 & 40.5 \\ YOLOv9 & 88.2 & 42.6 \\ Ours & **90.5** & **45.5** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative test results (%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mAP50** & **mAP50:95** \\ \hline SSD & 76.0 & 50.3 \\ Faster-RCNN & 96.3 & 70.8 \\ CenterNet & 88.6 & 65.9 \\ YOLOv3-tiny & 92.4 & 69.5 \\ YOLOv5s & 93.3 & 70.1 \\ YOLOv7 & 93.6 & 68.7 \\ YOLOv9 & 95.7 & 71.3 \\ Ours & **97.2** & **72.6** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparative test results (%).  
dimensions. The generalization ability and flexibility of the model improved. Subsequently, the asymptotic feature pyramid network AFPN was used in the feature fusion strategy, which starts from the bottom layer and gradually proceeds upward until it obtains a rich multi-scale feature representation, effectively solving the problem of inconspicuous feature information of small objects of the road indicator. Finally, to solve the problem of imbalance between difficult and easy samples, the Focaler-IOU loss function was introduced, which improved the attention to the difficult-to-detect samples when calculating the loss and then greatly improved the performance of the algorithm model. In the experimental stage, on the one hand, experimental validation was carried out for the homemade headlight dataset, and the mAP50 and mAP50:95 reached 90.5% and 45.5%, respectively, which are 2.9 and 3.6 percentage points higher than the YOLOv8 model, and good experimental results were achieved. On the other hand, two public datasets were selected--the S2 TLD dataset and the BSTLD dataset--both are traffic signal light datasets that face the same problem with headlight object detection, and the same experimental results were good on the two datasets, and accuracy was improved to a certain extent, which shows that generalization and portability can excellently fulfill the detection task of road indicator lights. In addition, the ablation experiment proves the effectiveness of the improvements in this paper, and the comparison experiment demonstrates the advancements of the algorithmic model in this paper to the mainstream model on the headlight dataset and the S2 TLD dataset, and the results of the research in this paper provide feasible strategies for realizing more intelligent assisted driving, which will achieve good benefits in practical applications in the future.  
Methodology, Z.Y.; Validation, Z.H.; Investigation, Z.Y.; Data curation, P.T.; Writing--original draft, P.T.; Writing--review & editing, Z.Y. and J.S.; Visualization, Z.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data set in this paper was taken in a private place, which involves the privacy of other vehicles, so this data set is not disclosed.  
The authors declare no conflict of interest.