# Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes
## 3 Results  
### Datasets and Evaluation Metrics  
Three datasets were selected as the experimental data for the article, two of them are traffic signal light public datasets S2 TLD and BSTLD, and the other one is a homemade dataset of headlights in a complex environment. All of them are image datasets in real scenes, and the labeled object information satisfies the definition of small object detection. The traffic signal dataset covers a wide range of different driving scenarios, including city streets, suburban roads, highway intersections, and crossings, as well as different lighting and weather conditions (e.g., sunny, rainy, nighttime). Each image provided an annotation of the location of the traffic signal, as well as the current color status of the signal. The summarized information for the three datasets is shown in Table 1.  
The headlamp dataset was the data information collected in the system of a research institute of the Ministry of Public Security in a real scene, including headlamp data in different weather, different angles, different workstations, different models (both large and small vehicles), and indoor and outdoor situations. A total of 18,815 images are included, of which 16,034 are used as the training set, 1781 as the test set, and 1000 as the validation set; they are divided into the three different types of labels most commonly used in vehicles: headlamp, stoplight, and turnlamp, where the number of individual classified annotations are 13,350, 10,012, and 76,712, respectively, and 10,012, and 7679, respectively, for each category of labeling. As Figure 3 shows the visualization results of this dataset, most of the labels have relative widths between 0.01 and 0.03 and heights between 0.02 and 0.05, which are small in size, and this is one of the biggest challenges affecting the performance of headlight detection. As shown in Figure 4, an illustrative snapshot of the headlight dataset within the context of vehicle inspection environments, it becomes evident that the dataset encapsulates a diverse range of vehicles, encompassing not only compact models like cars and SUVs but also extensive ones such as trucks, buses, and vans, among numerous other varieties. This diversity, coupled with the inherent variations in the headlights across these distinct vehicle types, poses significant challenges for algorithmic models tasked with accurate detection. Specifically, the headlights represent one of three crucial light types, alongside brake lights and turn signals (both front and rear), underscoring the complexity of differentiating between them. Notably, the presence of multiple headlights simultaneously illuminated in various scenarios introduces substantial interference in the detection process, exacerbating the need for precision in distinguishing between different headlight sources. Furthermore, in some images, the headlights appear distant from the viewpoint, compounded by robust environmental distractions (including reflections, varying exposures, and other factors) that are inevitable in real-world scenarios. To tackle these multifaceted difficulties, the algorithmic model presented in this paper is dedicated to overcoming these challenges, thereby facilitating the advancement of computer vision-based intelligent detection in practical industrial applications. By addressing these complexities, our approach strives to enhance the reliability and efficacy of headlight detection systems in real-world settings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Classes** & **Train** & **Test** & **Dev** \\ \hline Lamp & 3 & 16,034 & 1781 & 1000 \\ S2 TLD & 4 & 3967 & 1128 & 1000 \\ BSTLD & 4 & 10,563 & 1864 & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of information for the three datasets. The described S2 TLD small-scale traffic signal dataset was provided by the Kaggle data platform. The dataset consists of 6095 images, similar to the headlight dataset, and the objects are in various complex conditions such as low pixels and bad weather.