Efficient and Accurate Traffic Sign Detection Leveraging YOLOv8: A Cutting-Edge Deep Learning Framework

Objectives: To present an automated computer vision-based traffic sign detection model created to identify roadside signs accurately in different environmental conditions. The goal is to improve detection accuracy using a lightweight architecture suitable for mobile devices. Methods: This study presents CV-TSD model. The technique integrates Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and TensorFlow-based machine learning for image recognition. Traffic sign datasets are collected from various online fora, annotated, and trained using convolutional neural networks. The proposed system uses Histogram of Oriented Gradients (HoG), Local Binary Pattern (LBP), and deep learning with TensorFlow for effective image classification. A custom dataset of traffic sign images was collected from several online sources, manually labelled, and used to retrain a convolutional neural network using transfer learning. Three environmental categories—dark shade, cloudy, and rainy—were used to test classification robustness. We assessed performance with training accuracy, validation accuracy, and cross-entropy loss. Findings: Experimental results show that CV-TSD consistently outperforms traditional HoG and LBP methods across all metrics. The system achieves a maximum training accuracy of 98% and a validation accuracy of 97% with a limited number of image samples. Furthermore, the model keeps stable cross-entropy values, indicating effective learning without overfitting. The lightweight design allows reliable performance even with smaller datasets, making it ideal for mobile and intelligent transportation systems. Novelty: Unlike standard traffic sign detection methods, CV-TSD mimics human visual perception by merging semantic image understanding with deep learning. Its ability to classify accurately with minimal data and low computational demands marks a significant advancement for real-time applications. Keywords: Traffic Sign Detection, Deep Learning, Image Classification, TensorFlow, HoG, LBP, Transfer Learning

Indian traffic sign detection and recognition using deep learning

Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments. First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed. To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map. Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map. The proposed improved region proposal network is designed for fast and accuracy proposal generation. In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection. Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.

A Convolutional Approach to Traffic Sign Detection presents a deep learning-based method using Convolutional Neural Networks (CNNs) for accurate traffic sign detection and classification. The system follows a two-stage pipeline: region proposal and sign classification, leveraging data augmentation and transfer learning for improved performance. It demonstrates robustness to challenges like poor lighting and occlusion and supports real-time operation, making it suitable for ADAS and autonomous vehicles. Experimental results show high accuracy across datasets, highlighting its potential in enhancing road safety and intelligent transportation systems. Index Terms — Traffic Sign Detection, Convolutional Neural Networks (CNN), Deep Learning, Computer Vision, GTSRB, Image Classification, Object Detection, Real-Time Detection, Autonomous Driving, Data Augmentation, OpenCV, Keras, TensorFlow, Image Preprocessing, Road Safety

Traffic sign detection plays an important role in a traffic sign recognition system. It is also an unsolved problem in the intelligence transportation system. Two main technique routes are typically used in current research of the traffic sign detection: the technique using gray-level object image or that using color object image. The former has the advantage that the related theories and methods are more mature, but the weakness is short of information, which results in more chances of misdetection. The latter has the advantage that more abundant information can be provided same as human sense of vision, but the weakness is that the related theories and methods are still immaturity. This research proposes a concept of color-shape pair based on the prior characteristic of the color and the geometric shape by analyzing the traffic signs that might appear in Chinese highways. Also, a model of traffic signs, color-geometric model, is given according to the relative definition of the Color-Shape Pair. This model is constituted with 5 kinds of basic shapes and 3 kinds of basic colors of the traffic signs, focusing on the unique determinacy that is the most important characteristics of color and geometric shape. Therefore, using the detection of traffic signs based on color-geometric model, we can realize the rough classification of the traffic signs. Furthermore, we can divide 116 kinds of traffic signs on Chinese road into 7 subclasses, and reduce the complexity of recognition system of road traffic signs. The simulation experiment results have shown that this method can achieve 100% correct ratio for the traffic sign detection and the rough classification, and the robustness and real-time performance are also improved.

In recent years, the deep learning is applied to the field of traffic sign detection methods which achieves excellent performance. However, there are two main challenges in traffic sign detection to be solve urgently. For one thing, some traffic signs of small size are more difficult to detect than those of large size so that the small traffic signs are undetected. For another, some false signs are always detected because of interferences caused by the illumination variation, bad weather and some signs similar to the true traffic signs. Therefore, to solve the undetection and false detection, we first propose a cascaded R-CNN to obtain the multiscale features in pyramids. Each layer of the cascaded network except the first layer fuses the output bounding box of the previous one layer for joint training. This method contributes to the traffic sign detection. Then, we propose a multiscale attention method to obtain the weighted multiscale features by dot-product and softmax, which is summed to fine the features to highlight the traffic sign features and improve the accuracy of the traffic sign detection. Finally, we increase the number of difficult negative samples for dataset balance and data augmentation in the training to relieve the interference by complex environment and similar false traffic signs. The data augment method expands the German traffic sign training dataset by simulation of complex environment changes. We conduct numerous experiments to verify the effectiveness of our proposed algorithm. The accuracy and recall rate of our method are 98.7% and 90.5% in GTSDB, 99.7% and 83.62% in CCTSDB and 98.9% and 85.6% in Lisa dataset respectively.

Abstract. TSR (Traffic sign recognition) is an important problem in ITS (intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and unmanned vehicle etc. TSR consists of two steps: detection and recognition, and this paper describe a new traffic sign detection method. The design principle of the traffic sign is comply with the visual attention mechanism of human, so we propose a method using visual attention mechanism to detect traffic sign ,which is reasonable. In our method, the whole scene will firstly be analyzed by visual attention model to acquire the area where traffic signs might be placed. And then, these candidate areas will be analyzed according to the shape characteristics of the traffic sign to detect traffic signs. In traffic sign detection experiments, the result shows the proposed method is effectively and robust than other existing saliency detection method.

With the notion of smart cities transforming cities into digital societies and making people's lives easier in every way, Intelligent Transportation Systems have become an integral element among all. The Intelligent Transportation System (ITS) attempts to improve traffic efficiency by reducing congestion and ensuring the safety and comfort of commuters in real time. Traffic sign detection and recognition is one of the multifaceted conjunctive fields of research in ITS. In this paper, we address the issue of the TSR (traffic sign recognition) problem, i.e., classification of traffic signs along the roadside which plays a crucial role in developing advanced driver assistance and autonomous driving systems. CNN's network design has a huge impact on its performance and convergence. As a result, we use the Genetic Algorithm (GA) to automate the task of selecting a high-performance CNN (Convolutional Neural Network) Architecture for the GTSRB (German Traffic Sign Recognition Benchmark) dataset. The model is optimized through GA using multiple network configurations in the search space. Our model takes into account the limitations of the dataset, and we use certain data augmentation approaches to address the issues. We were able to attain an average accuracy of 98.2% which demonstrates the state-of-the-art performance on the publicly available dataset.KeywordsConvolutional neural networkGenetic algorithmMulticlass classificationDeep learningModel optimization

The traditional traffic sign detection algorithm can not deal with the application scenarios such as intelligent transportation system or advanced assisted driving environment, and it is difficult to meet the application requirements in detection accuracy and efficiency. Focusing on the above problems, this paper proposes a traffic sign detection algorithm based on Single Shot Multibox Detector (SSD) combined with Receptive Field Module (RFM) and Path Aggregation Network (PAN). The proposed algorithm is abbreviated to SSD-RP. The SSD-RP uses the RFM to improve the receptive field and semantics of the predicted feature maps, thus improving the detection performance of small traffic signs. At the same time, the path aggregation network is introduced to integrate multiscale features, which makes the abstract semantic information and rich detailed information shared among multiscale feature maps, enhances the discrimination ability of feature system, and improves the location and classification accuracy of traffic signs. Following that, the spatial pyramid pooling module is used to pool the shallow features and integrate them into the bottom-up information transmission path of the path aggregation network, thus continuing to supplement the fine-grained features for the feature system and further improve the detection performance. The experimental results on GTSDB and CCTSDB data sets show that SSD-RP has higher mean average precision (mAP) than traditional SSD algorithm and can better detect small traffic signs, which means that SSD-RP has higher detection precision. In addition, the experimental results also show that, compared with the common object detection algorithms such as Faster R-CNN, RetinaNet, and YOLOv3, the SSD-RP achieves a better balance between detection time and detection precision.

The use of computer vision technology for our system has taken a huge leap and has revolutionized the way in which road safety is managed and maintained. This part of the paper represents the key components and functionalities of such a system which helps in enhancing the road safety and also in traffic management. The proposed system is meant to contain computer vision algorithms and some machine learning techniques. These will help in detecting and recognizing the traffic signs in real time video streams. With the help of deep learning models like convolutional neural networks ,the proposed system can precisely identify various traffic signs present on roads.The major components of the proposed system include a camera interface which will capture real time video frames from the road, image processing modules for colour space conversions, binary thresholding, contour detection and a classification module for the purpose of sign recognition. Deep learning frameworks like TensorFlow and OpenCV libraries are used for image processing and model training In the proposed system. The real time nature of this system allows the spontaneous detection and recognition of traffic signs enabling us to change the road conditions. Hence, the system contributes to prevent accidents and provide timely warnings to drivers. In addition, this system offers potential applications in traffic management and for autonomous driving system. With the help of accurate information, the system can contribute towards more efficient traffic flow and reduce congestion on roads. Overall, the system has very good impacts in road safety and efficiency. It shows advancement in road safety and traffic management.

Traffic sign detection is crucial in intelligent transportation and assisted driving, providing favourable support for driving safety and prevention of traffic accidents. Aiming at the current traffic sign detection problems of leakage, misdetection and low detection accuracy of small targets, a traffic sign detection method based on improved YOLOv8n is proposed. Firstly, the Neck part of YOLOv8 is improved by designing a module that combines Attention Scale Sequence Fusion with the P2 small target detection layer (AFP) to enhance the feature extraction capability of the YOLOv8 network, enabling it to capture more small target features. Secondly, a lightweight convolution module, LWConv, is designed, based on which the Bottleneck structure of Cross-convolution with two filters (C2f) in YOLOv8 is reconstructed and named LW_C2f, effectively reducing the model size and parameters. Finally, the loss function of the original YOLOv8 is replaced with the Wise-IoU loss function, which improves the network’s bounding box regression performance and reduces the negative impact of low-quality samples. The experimental results show that the mean average precision (mAP50) of the improved model on the TT100K dataset is increased by 5.7% compared to the YOLOv8 model, while the number of parameters and the model size are reduced by 0.6 M and 0.8 MB, respectively.

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.

Considering the problem of automatic information acquisition in the field of intelligent transportation system (ITS), a new approach for detection of road traffic sign from natural scene images is proposed in this study. The adaptive colour segmentation based on pixel vector is firstly used to segment colour image into binary image and stand out traffic sign regions, which can reduce the influence of lighting conditions on image segmentation. Secondly, to improve the ability of shape identification during traffic sign detection, central projection transformation (CPT) is used to compute shape feature vectors of different candidate regions, and this shape feature is input to the probabilistic neural networks (PNN) to discriminate true traffic signs from candidates. The proposed approach is applied to many natural images. Experimental results show that the proposed method can effectively detect road traffic signs from natural scene images.

Emerging advances from artificial intelligence, hardware accelerators, and processing architectures continue to transform societal challenges impacted by geospatial applications. Recent breakthroughs in deep learning have brought forward an automated capability to learn hierarchical representational features from massive and complex data, including text, images, and videos. In tandem, rapid innovations in sensing technologies are collecting geospatial data in even higher resolution and throughput to enable mapping and analysis of the earth's surface, events, and various phenomena in unprecedented detail. When integrated, these developments offer potential breakthrough opportunities in geographic knowledge discovery geared to impact better decision making. The outcomes have broader implications, from humanitarian mapping, intelligent transport systems, urban expansion analysis, spatial diffusion methods to support epidemiology, climate change-induced threats, natural disasters, and monitoring of the earth's surface.

Detecting small traffic signs poses significant challenges due to the complex nature and dynamic conditions of real-world traffic scenarios. In response to these challenges, we propose an improved YOLOv5s target detection model incorporating the multilevel squeeze feature perception (ML_SAP) mechanism, aiming to increase the accuracy of small traffic sign detection. First, an additional detection layer is incorporated to enhance the model’s capacity for detecting small-scale traffic signs. This improvement is accompanied by the adoption of a WIoU loss function, which evaluate the quality of anchor boxes. Moreover, the ML_SAP mechanism is designed to promotes the fusion and extraction of features at different levels. This mechanism effectively increases the network model’s proficiency in identifying small targets under varying environmental conditions. To verify the effectiveness of the improved method, we conducted extensive experiments on two public transportation sign datasets. Notably, on the challenging samples in the CCTSDB-2021 dataset, the improved model achieves a detection recall of 77.1%, which is 5.4% higher than that of YOLOv5s, and a mean average precision (mAP) of 82.7%, which is 3.9% higher than that of the base model. Furthermore, the model achieves a detection recall of 91.3% on the TT100K dataset, which is 3.7% higher than the performance of YOLOv5s, and a mean precision (mAP) of 91.5%, which is 4.6% higher than that of the base model.