# Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes
## 1 Introduction  
Indicator lights encountered by vehicles navigating on the road can be broadly categorized into two distinct types, that is, those emanating from other vehicles, specifically headlights, and traffic signals. In the rapidly evolving landscape of autonomous driving [1] and intelligent transportation systems [2], the pursuit of accurate and real-time road indicator object detection technology [3] has emerged as a cornerstone for ensuring driving safety and enhancing traffic efficiency. This pursuit becomes all the more crucial in complex road scenarios, such as those occurring during nighttime, adverse weather conditions,and high-density traffic situations, where the detection of traffic signals and headlights is paramount. Headlights serve as not only a defining feature of vehicles but also a vital source of auxiliary information for driving safety. They effectively convey the vehicle's movement status and intention, thereby enhancing situational awareness. Similarly, traffic signals play a pivotal role in guiding vehicles safely through intersections. Thus, the development of an efficient, accurate, and adaptable object detection algorithm specifically tailored for road indicator lights in such complex scenes holds immense significance. It promises to elevate the accuracy and efficiency of vehicle detection processes, ultimately enhancing the performance and reliability of automated driving systems. However, the detection of road indicator lights within intricate scenarios poses a multitude of challenges. Firstly, both vehicle lights and traffic signals fall under the category of small object detection [4], inherently subject to constant fluctuations. Furthermore, the low-light environment prevalent at nightfall or under adverse weather conditions significantly deteriorates image quality, directly compromising the precision of detection. Notably, the diverse shapes, sizes, and brightness levels exhibited by various types and brands of vehicle lights [5] further complicate the detection process. Additionally, the blurring and obscuring effects caused by high-speed vehicles and dense traffic flows not only hinder accurate light recognition but also induce false detections. Current road light detection algorithms [6], leveraging either traditional image processing techniques or deep learning methodologies, have witnessed some advancements. Nevertheless, they still fall short in addressing the aforementioned complexities, particularly in terms of real-time performance and robustness, which necessitates further refinement. Within the realm of detection algorithms based on deep learning, two primary categories exist: two-stage and one-stage algorithms. Two-stage algorithms [7], exemplified by R-CNN [8], Fast R-CNN [9], and Faster R-CNN [10], employ candidate regions for target detection. In contrast, one-stage algorithms [11], represented by the YOLO series [12; 13; 14; 15; 16], achieve simultaneous localization and classification, offering a more streamlined approach. To tackle these challenges head-on, this study proposes a road indicator object detection algorithm tailored for complex environments, aimed at enhancing detection performance. By integrating an attention mechanism [17], we bolster the model's [18] capacity to discern car light features, while multi-scale processing [19] enhances the feature fusion ability for minute light objects. Furthermore, we optimize the algorithm's structure and computational flow to ensure swift processing speeds [20], thereby satisfying real-time requirements. Furthermore, this algorithm excels in capturing lighting features across multiple scales, leveraging the Asymptotic Feature Pyramid Network (AFPN) module, built upon the robust YOLOv8 model. This AFPN module facilitates seamless feature fusion, ensuring that even the finest nuances of light variations are captured and analyzed. Additionally, the incorporation of an Attention Mechanism module is a game changer.