## 2 Related Work  
Object detection for ADAS has gained many developments in recent years for greater safety and efficiency in autonomous driving. Various studies have focused on strengthening the resilience, accuracy, and real-time capabilities of object detection models optimized for ADAS applications. A study by [23] shows the state-of-the-art object identification and recognition task with tracking algorithms used in ADAS, which focuses on the application to diverse sensor data inputs such as cameras, LiDAR, and radar. The study illustrates recent accomplishments and identifies future research directions in object detection for ADAS. The disagreement between single-stage and two-stage object detectors continues to drive the development of ADAS systems. Single-stage detectors like YOLO and its descendants have acquired favor because of their lower complexity and faster inference times, making them appropriate for real-time applications in ADAS. Despite their simplicity, these models often obtain competitive accuracy compared to two-stage detectors, which are normally more complex but also more powerful. The study shown in [24] discusses the architecture advancements and performance metrics of single-stage and two-stage detectors. The YOLO series of models, mainly YOLOv5 [25], has been extensively used for real-time traffic sign detection because of its speed and accuracy. An improved YOLOv5 network was proposed to handle multi-scale traffic signs, with a feature pyramid network and an attention detection head to enhance feature representation. This model demonstrated superior performance on the TT100K dataset with a mean average precision (mAP) of 65.14% and an FPS of 95. A study in [26] proposed a YOLOv5 model with global feature extraction capabilities and a multi-branch lightweight detection head to increase small traffic sign detection accuracy. Also, [27] proposed a model with the YOLOv5s variant to overcome ambient lighting interference and target size changes by introducing an optimization algorithm called ETSR-YOLO, Path Aggregation Networks (PANet) to enhance multi-scale feature fusion, and C3 modules to suppress background noise interference. The work [28] displays multi-scale traffic sign detection in difficult situations using a modified YOLOv4 feature pyramid structure to increase the information transmission between deep and shallow features, boosting the representation capabilities of feature pyramids. The performance of this work is measured by the mean average precision (mAP) metric, which is found to be 81.78% for both the Tsinghua-Tencent 100K dataset and a bespoke dataset. M-YOLO [29] presents an approach to enhance traffic signs that are distorted, impacted by light, or distant. This is achieved by minimizing the computing burden of the network and accelerating the process of extracting features. The YOLOv5I model demonstrated superior performance compared to the Chinese Traffic Sign Detection Benchmark (CCTSDB) dataset. YOLOv5-TS [30] presents a solution to address the issue of inaccurate or incomplete predictions for small objects. This is achieved by enhancing the spatial pyramid structure through the utilization of depthwise convolution and substituting maximum pooling procedures in spatial pyramid pooling. A module for multiple feature fusion is presented to repeatedly combine multi-scale feature maps in order to enhance the final features. A work in [31] integrates YOLOv8 with the Segment Anything Model (SAM) to produce a hybrid model of object recognition and segmentation that can detect items with complicated visual properties. A study in [32] shows the implementation of traffic sign detection on an edge device by fusing the Ghost module and the Efficient Multi-Scale Attention module into YOLOv8, such that the model may improve the computing speed while keeping the original characteristics. Capsule networks for traffic sign detection [33] are also part of a novel approach that addresses the problem of traditional CNNs in capturing poses and spatial hierarchies.