Traffc Signs Detection using YOLOv8: A Real-Time Deep Learning Approach for Autonomous Driving Systems

ADVANCED REAL-TIME TRAFFIC SIGN SEGMENTATION AND CLASSIFICATION USING HYBRID DEEP CONVOLUTIONAL ARCHITECTURES ON GTSDB AND BENCHMARK DATASETS

Autonomous driving systems and Advanced Driver Assistance Systems (ADAS) heavily rely on precise visual recognition of traffic control infrastructure under varying real-world conditions. While traditional object detection methods utilize bounding boxes, pixel-level semantic and instance segmentation provide essential geometric detail required for motion planning and vehicle control. This paper presents a rigorous comparative analysis of deep learning models, specifically U-Net, Mask R-CNN, and YOLOv8-Seg, for real-time traffic sign segmentation. Training and evaluation were conducted on benchmark datasets including the German Traffic Sign Detection Benchmark (GTSDB) and TT100K. To address extreme pixel-level class imbalance inherent in roadside imagery, a hybrid compound loss function combining Focal Loss and Dice Loss was developed. Experimental results indicate that YOLOv8-Seg achieves an optimal trade-off with a mean Average Precision (mAP50) of 89.7% and Intersection over Union (IoU) of 81.3% while operating at 45.2 Frames Per Second (FPS) on standard embedded GPU hardware. U-Net demonstrated superior boundary reconstruction accuracy with an IoU of 82.1%, whereas Mask R-CNN achieved peak precision (91.2% mAP50) at reduced inference speeds (12.4 FPS). The source code implementation and optimized hyperparameter setup in PyTorch 2.0 are detailed.

Development of An Artificial Neural Network-Based System to Detect Lane and Roadside Traffic Signs

Ensuring road safety requires timely and accurate detection of lanes and roadside traffic signs, which are essential for assisting drivers and reducing accident risks. However, robust simultaneous detection remains challenging due to variations in illumination, occlusions, complex backgrounds, and diverse road conditions. This research introduces a novel vision-based system for lane detection and roadside traffic sign recognition using advanced artificial neural network architectures. In the pre-processing stage, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to enhance image visibility under varying lighting conditions. For classification, a Self-Artificial Attentive Gated Neuro-Recurrent Unit (SAAG-NRU) is proposed, a novel architecture integrating deep artificial neural network (ANN) layers, Gated Recurrent Units (GRU), and a self-attention mechanism, enabling sequential refinement of features and prioritization of the most safety-relevant cues. In addition, YOLOv11 was utilized to train and detect traffic signs exclusively. For lane detection, a lane mask-based approach is implemented to accurately segment and highlight lane boundaries. The trained model was integrated into a Tkinter-based Graphical User Interface (GUI) to visualize real-time detection outputs effectively. Implemented using Python-based tools, the framework is validated against benchmark lane and traffic signs, demonstrating better performance compared to existing systems in terms of lane detection, achieving 91.11% precision, and traffic sign detection, attaining 96.88% precision. Therefore, the proposed system delivers fast, accurate, and robust simultaneous lane and traffic sign detection, significantly improving real-time road safety and driver assistance.

One-Stage Object Detectors in Autonomous Driving

Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a new detection system. The survey reviews the evolution of major one-stage detectors, including YOLOv1, SSD, RetinaNet, EfficientDet, anchor-free detectors such as FCOS and CenterNet, and recent real-time models such as YOLOv10. It compares these architectures through their design choices, feature-fusion strategies, loss functions, deployment trade-offs, and reported benchmark performance. The paper also summarizes commonly used autonomous-driving datasets, evaluation metrics, open challenges, and future research directions. Overall, this survey highlights how one-stage detectors balance speed, accuracy, efficiency, and robustness, while also emphasizing the remaining gap between benchmark results and dependable real-world autonomous-driving performance.

Region-Adaptive Traffic Sign Detection using Enhanced YOLOv8 with Small-Object Optimization

Traffic sign detection is an integral part of Advanced Driver Assistance Systems (ADAS) and self-driving cars wherein correct and timely detection helps ensure a safer driving experience. However, currently available models are trained on benchmark datasets that pertain to European or Chinese traffic scenarios. Such models lack the efficiency required for traffic scenarios in India due to multiple factors such as the variety of sign shapes and languages present, occlusions, varied light intensities and prevalence of smaller objects. In this paper, a region-based traffic sign detection system has been proposed using the enhanced YOLOv8 framework for Indian traffic scenarios. A subset of the Mapillary Traffic Sign Dataset (MTSD) has been curated, annotated and data-augmented with mosaic augmentation, change in brightness, adaptive scaling, and high-resolution training to overcome challenges posed by occlusions, varying light intensity and distant traffic signs.

Real-time object detection for autonomous driving: a comparative study of YOLO and Faster R-CNN

Over the past few years, object detection has experienced remarkable progress and development, primarily driven by the development of one-stage and two-stage detection algorithms. Among these, Faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO) have achieved notable success due to their strong performance and computational efficiency. Object detection plays a crucial role in various applications, particularly in autonomous driving systems, where accurate detection of pedestrians, vehicles, and road signs is essential for ensuring safety and reliability. This paper conducts a comparative evaluation of YOLO and Faster R-CNN to analyze their performance in autonomous driving environments. The experiments were conducted using the KITTI open-source dataset, which is widely used for benchmarking object detection models. All experiments were performed on an NVIDIA RTX A5000 GPU to ensure efficient computation, with implementations developed using Python version 3.9.13. The experimental findings indicate that YOLO surpasses Faster R-CNN in performance, attaining an accuracy rate of 90%. These findings highlight the effectiveness of YOLO for real-time object detection tasks, making it a suitable and preferred choice for time-sensitive applications such as autonomous driving systems.

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