Traffic Sign recognition for smart vehicles based on lightweight CNN implementation on mobile devices

Effective disease control in crops is essential for ensuring food security, as numerous diseases can significantly reduce agricultural productivity. Leaf disease is a crucial determinant of crop yield and quality. Potatoes are widely regarded as a vital staple in diets around the world. Potato blight, often known as late blight, is a severe disease impacting Solanaceae plants, particularly the potato. The oomycete Phytophthora infestans serves as the causal agent, posing a significant threat to potato crops by reducing yields and leading to financial losses. Identifying potato diseases is essential for ensuring food security and minimizing economic losses in agriculture. Numerous studies have explored the applications of machine learning and deep learning technologies for detecting blight diseases in affected crops. The proposed methodology outlined in our study offers a promising and effective means to enhance the accuracy of potato late blight classification. Advancements in technology can significantly improve the identification and treatment of diseases. Deep learning algorithms have been effectively employed in a range of practical applications. In our investigation, convolutional neural network models are used to analyze images of leaves. The primary aim of this initiative is to gain insights into and enhance the capacity to recognize diseases in their early phases. After the input images undergo preprocessing, the target region is distinctly isolated. CNN examines images to identify unique features, including edges, colours, and textures. This study evaluates the effectiveness of the proposed model through a diverse dataset of potato leaf images, making essential contributions to the domain of automated disease detection systems. The results of this study could enhance techniques for agricultural management and strengthen the sustainability of agriculture. This study introduces an improved CNN model designed to identify the blight disease state in potato leaf images, providing a valuable tool for farmers. The findings may aid in the evaluation and implementation of Convolutional Neural Network (CNN) models for classifying proximal sensing images of potato plant leaves. Additional investigations could leverage this modelling framework and have the opportunity to examine various other factors to identify leaf infections at an earlier stage for crop protection.

Advanced driver assistance systems (ADASs) and autonomous vehicles are expected to increase safety, lower energy and fuel consumption, and lower pollutants from road traffic. The advanced driver assistance system’s major features include lane detection and tracking. Finding color line marks on the road is the technique of lane detection. The process of lane tracking aims to help the vehicle continue traveling along a predetermined course. Hence, automatic detection of lanes using convolutional neural networks (CNNs) models has gained popularity in the current economic development. This paper also aims at detecting various objects using convolutional neural networks. In this work, we offer object classification and detection, a demanding topic in computer vision and image processing. As a result, we deployed convolutional neural networks on the Keras platform with TensorFlow support. The experimental results illustrate the amount of time needed to train, test, and generate the model using a constrained computing environment. Here, we trained the system for about 80 images which have taken a couple of seconds to detect with better accuracy. Traffic sign recognition is carried out, which is a significant area of research in ADAS. It is crucial for driverless vehicles and is frequently used to read stationary or moving road signs along the side of the road. A comprehensive recognition system is made up of traffic sign detection (TSD) and categorization (TSC). The paper also aimed at traffic sign recognition, which is crucial to consider, because traffic sign recognition is typically applied to portable devices. The model’s detection accuracy is ensured as long as the speed is maintained. The model developed in this study is 99.89% accurate.

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

Convolutional neural networks (CNNs) have demonstrated remarkable performance in many areas but require significant computation and storage resources. Quantization is an effective method to reduce CNN complexity and implementation. The main research objective is to develop a scalable quantization algorithm for CNN hardware design and model the performance metrics for the purpose of CNN implementation in resource-constrained devices (RCDs) and optimizing layers in deep neural networks (DNNs). The algorithm novelty is based on blending two quantization techniques to perform full model quantization with optimum accuracy, and without additional neurons. The algorithm is applied to a selected CNN model and implemented on an FPGA. Implementing CNN using broad data is not possible due to capacity issues. With the proposed quantization algorithm, we succeeded in implementing the model on the FPGA using 16-, 12-, and 8-bit quantization. Compared to the 16-bit design, the 8-bit design offers a 44% decrease in resource utilization, and achieves power and energy reductions of 41% and 42%, respectively. Models show that trading off one quantization bit yields savings of approximately 5.4K LUTs, 4% logic utilization, 46.9 mW power, and 147 μJ energy. The models were also used to estimate performance metrics for a sample DNN design.

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

This paper analyzes and studies the task of traffic sign recognition in the complex traffic road environment, analyzes the defects of the traditional classification algorithm and CNN (convolutional neural network) algorithm, cannot maximize the elimination of the lack of use of image disturbance information and hidden image information. This paper proposes a traffic sign recognition algorithm based on CNN, PCA (Principal Component Analysis) and LVQ (Learning Vector Quantization) algorithm, and uses PCA algorithm and LVQ algorithm to perform eigenvectors generated by CNN model. Dimensionality reduction and classification processing eliminates picture interference information and in-depth use of hidden information in pictures, further improves the accuracy of identification of traffic signs in complex environments, validates the effectiveness of the proposed method on the GTSRB data set, and relates to depth volumes. The product network model, LeNet-5, performs comparative experiments and proves that this method can further improve the classification accuracy.

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

Deep convolutional neural networks (CNN) have the strength in traffic-sign classification in terms of high accuracy. However, CNN models usually contains multiple layers with a large number of parameters consequently leading to a large model size. The bulky model size of CNN models prevents them from the wide deployment in mobile and portable devices in Intelligent Transportation Systems. In this paper, we design and develop a portable convolutional neural network (namely portable CNN) structure used for traffic-sign classification. This portable CNN model contains a stacked convolutional structure consisting of factorization and compression modules. We conducted extensive experiments to evaluate the performance of the proposed Portable CNN model. Experimental results show that our model has the advantages of smaller model size while maintaining high classification accuracy, compared with conventional CNN models.

Convolutional neural network (CNN) models have been used extensively in many image recognition tasks for their state-of-the-art performance in recent years. Researchers inspired by this success frequently prefer CNNs in the agricultural field, especially for disease detection and classification. Many CNN models have been proposed for plant leaf diseases and impressive performance results have been obtained. On the other hand, standard CNNs usually need millions of parameters in the network for computation, but it is difficult to implement them on embedded and mobile devices with limited resources. Therefore, it is important to obtain lighter models by decreasing the number of parameters in addition to the high performance of the models. In this paper, a new hybrid CNN approach based on Inception architecture and depthwise separable convolutions is proposed to reduce the number of parameters and computational cost for plant leaf disease detection and classification. Although the number of parameters is significantly reduced, the results show that the proposed approach has high accuracy performance. The proposed hybrid model has been trained and tested with k-fold cross-validation using a dataset of 50,136 images containing 30 classes from 14 different leaves, including healthy and diseased ones. The new model has achieved the best accuracy of 99.27% and an average accuracy of 99%, and provides about a 75% reduction in the number of parameters compared to the standard CNN.

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

In this paper, we propose a traffic sign recognition algorithm based on support vector machine (SVM) and convolutional neural network (CNN) for the traffic sign recognition of intelligent transportation and unmanned vehicle. In the detection of traffic signs, by using color features, firstly we convert the RGB color space to HSV color space, which can get the regions of interest (ROI). Then extract the Histogram of Oriented Gradient (HOG) features, and determine whether it is traffic sign by support vector machine (SVM). This algorithm can successfully detect traffic signs with a high detecting rate. The following is the classification of the detected traffic signs, by using a kind of convolutional neural network, which fuse the RGB information of a picture. In the end, the `German traffic signs' dataset is used to verify the accuracy. Experimental results show that the proposed algorithm can effectively recognize traffic signs, achieving high accuracy and the algorithm complexity is lower.

Traffic sign recognition is an important step for integrating smart vehicles into existing road transportation systems. In this paper, an NVIDIA Jetson TX1-based traffic sign recognition system is introduced for driver assistance applications. The system incorporates two major operations, traffic sign detection and recognition. Image color and shape based detection is used to locate potential signs in each frame. A pre-trained convolutional neural network performs classification on these potential sign candidates. The proposed system is implemented on NVIDIA Jetson TX1 board with web-camera. Based on a well-known benchmark suite, 96% detection accuracy is achieved while executing at 1.6 frames per seconds.

Technological advancements have had a significant impact on the education sector, including the application of Convolutional Neural Networks (CNN) for facial image analysis. This research aims to implement CNN to measure students' learning concentration levels. The FER2013 dataset, which includes seven emotion classifications and comprises 28,709 images for training data, is used as the database. The data is processed through rescaling and augmentation to prepare the CNN model. The model consists of several convolutional layers, pooling layers, and fully connected layers designed to extract crucial features from facial images. Evaluation results demonstrate a very high accuracy of 94.95% on training data, indicating that the model effectively recognizes complex patterns within the data. Although there is a higher loss value of 157% and a decreased accuracy of 62.75% on validation data, this suggests that the model possesses a strong foundational capability and can still be improved through further adjustments. With high accuracy in training and promising validation results, the model shows substantial potential for real-world application, where it can assist teachers in understanding students' emotional responses in real-time. The implementation of CNN aids educators in comprehending students' emotional responses and adapting their teaching methods more effectively, thereby creating a more conducive learning environment and enhancing students' academic and social development. These findings also open opportunities for further research to improve the performance and generalization of the model on unseen data, making this technology an increasingly reliable tool in education

Considering that most convolutional neural network (CNN) models designed for traffic sign recognition (TSR) have sacrificed more resources and complicated network model development while pursuing higher performance, LeNet-5 shallow CNN with low complexity has been selected for improvement. Increasing the number of convolution kernel in the first convolution layer (C1 layer) and the third convolution layer (C3 layer) while reducing the size of the convolution kernel in C3 layer. Introducing Rectified Linear Unit (ReLU) function with better performance. The maximum pooling is introduced instead of mean pooling. Besides, the output layer employs support vector machine (SVM) to shorten the operation time. The research results demonstrate that the improved LeNet-5 network has an identification accuracy rate of 98.12% and the identification time is 0.154s for traffic signs in German Traffic Sign Recognition Benchmark (GTSRB), which could guarantee the real-time performance of the system and effectively reduce the complexity of the system on the basis of a high recognition rate.