Car Images Research Articles

Detection dataset of electric bicycles for lift control

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the “whole-electric-bicycle annotation” method, which could help with the development of electric bicycle detection applications in lifts.

CARS Imaging to Evaluate Neurodegenerative and Demyelinating Activity

CARS Imaging to Evaluate Neurodegenerative and Demyelinating Activity

Road car image target detection and recognition based on YOLOv8 deep learning algorithm

In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.

Strategi Komunikasi Pemasaran Digital CV. Taha Citra Mobil Dalam Meningkatkan Penjualan Mobil diKota Medan

This research discusses digital marketing communication strategies on CV. Taha Car Image. CV is an abbreviation of Commanditaire Vennootschap, in Indonesian it is also called Commander's Fellowship. This research aims to observe strategies that can increase sales carried out by the company. CV. Taha Citra Mobil is one of the CVs that operates in the field of buying/selling used cars, which can create a positive impact between parties to build long relationships, consumers and stakeholders. This research uses qualitative methods with interviews and analysis to gain a competent understanding of the digital marketing communication strategy implemented by Taha Citra. The research results prove that the strategy can be implemented perfectly by involving consumers through social media and e-commerce platforms, displaying creative, innovative content and existing facts and establishing cooperative relationships. It is known that the use of functional tools is very useful for measuring the extent of marketing effectiveness. According to these findings, it is hoped that competitors can provide updated activities on digital platforms through the media, establishing good relationships with consumers by providing content that is educational and interesting. Therefore, this research provides benefits for competitors to develop more innovative marketing communication strategies in the future.

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.

USE OF INTELLIGENT FUZZY IMAGE SEGMENTATION SYSTEMS IN THE PROFESSIONAL TRAINING OF FUTURE SPECIALISTS IN ENGINEERING AND PEDAGOGICAL FIELDS

Relevance: The article addresses the critical issue of integrating intelligent image segmentation systems that utilize fuzzy logic into the training processes for future specialists in engineering and pedagogical fields. This integration is a significant aspect of the digitization of higher education. Aim: The goal is to implement intelligent vehicle image segmentation systems using fuzzy logic to train specialists in engineering and pedagogical fields. Methods: The preliminary processing of the images of the studied objects (vehicles) involved digital filtering methods, contour detection, profile analysis, and contrast enhancement. Image segmentation was performed using watershed methods, contour lines, and region growing. After segmentation, the obtained segments were selected based on size. Fuzzy membership functions were then applied to determine the degree of affiliation of the segments to the meaningful parts of the studied objects, ensuring reliable recognition of these parts and stable operation of the intelligent system despite external influences on the acquired images. Results: A computer system has been developed for the segmentation of vehicle images using fuzzy logic, which has been integrated into the training of specialists in engineering and pedagogical fields. The segmentation methods isolate objects within the images, which are then recognized using fuzzy logic. Thanks to the fuzzy membership functions, elements of vehicle images are reliably recognized even when there is some ambiguity in the shapes of the segments. The practical significance of the developed system is demonstrated through the processing of car images. Conclusions: The integration of the developed system into the educational process provides students with both theoretical knowledge and practical skills related to intelligent image processing systems.

Physically‐Based Lighting for 3D Generative Models of Cars

AbstractRecent work has demonstrated that Generative Adversarial Networks (GANs) can be trained to generate 3D content from 2D image collections, by synthesizing features for neural radiance field rendering. However, most such solutions generate radiance, with lighting entangled with materials. This results in unrealistic appearance, since lighting cannot be changed and view‐dependent effects such as reflections do not move correctly with the viewpoint. In addition, many methods have difficulty for full, 360° rotations, since they are often designed for mainly front‐facing scenes such as faces. We introduce a new 3D GAN framework that addresses these shortcomings, allowing multi‐view coherent 360° viewing and at the same time relighting for objects with shiny reflections, which we exemplify using a car dataset. The success of our solution stems from three main contributions. First, we estimate initial camera poses for a dataset of car images, and then learn to refine the distribution of camera parameters while training the GAN. Second, we propose an efficient Image‐Based Lighting model, that we use in a 3D GAN to generate disentangled reflectance, as opposed to the radiance synthesized in most previous work. The material is used for physically‐based rendering with a dataset of environment maps. Third, we improve the 3D GAN architecture compared to previous work and design a careful training strategy that allows effective disentanglement. Our model is the first that generate a variety of 3D cars that are multi‐view consistent and that can be relit interactively with any environment map.

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Secure intelligent parking systems are complicated issues around the world. A secure parking system that is larceny-free is considered a luxury now. Parking is expensive and limited in almost all large cities. This research study presents a unique, safe, intelligent parking system based on deep image recognition techniques. To solve this tedious problem, an object detection deep learning model such as You Only Look Once (YOLO) is used. The proposed model is divided into three steps. The first step captures the car's image while entering the parking area. The second step is License plate recognition, in which the license plate is detected and saved the individual characters, and the recognized dataset is used when the driver leaves the parking area. The third step is facing detection and recognition of the taken plate. The fourth step is that the printer generates an invoice code used in case of a mismatch in the face of the driver. Evaluation of the proposed model indicates its efficiency. Accuracy of the following modules is achieved for vehicle detection (99%), number plate detection and recognition 98%, and 95%, respectively, and face detection and recognition 99%, and 97%, respectively. The proposed model's efficiency is shown through its evaluation. To get more security, a two-way screening procedure is used to keep the vehicles secure. So, the identification system's unique idea is to achieve better results by using two cameras and two-way authentication. It also forbids the entrance of those vehicles and drivers who are unauthorized or not allowed by the firm. Moreover, these modules were successfully implemented and evaluated using various performance measures, including precision, recall, and mean average precision.

Electrophysiological correlates of face and object perception: A comparative analysis of 2D laboratory and virtual reality conditions.

Human face perception is a specialized visual process with inherent social significance. The neural mechanisms reflecting this intricate cognitive process have evolved in spatially complex and emotionally rich environments. Previous research using VR to transfer an established face perception paradigm to realistic conditions has shown that the functional properties of face-sensitive neural correlates typically observed in the laboratory are attenuated outside the original modality. The present study builds on these results by comparing the perception of persons and objects under conventional laboratory (PC) and realistic conditions in VR. Adhering to established paradigms, the PC- and VR modalities both featured images of persons and cars alongside standard control images. To investigate the individual stages of realistic face processing, response times, the typical face-sensitive N170 component, and relevant subsequent components (L1, L2; pre-, post-response) were analyzed within and between modalities. The between-modality comparison of response times and component latencies revealed generally faster processing under realistic conditions. However, the obtained N170 latency and amplitude differences showed reduced discriminative capacity under realistic conditions during this early stage. These findings suggest that the effects commonly observed in the lab are specific to monitor-based presentations. Analyses of later and response-locked components showed specific neural mechanisms for identification and evaluation are employed when perceiving the stimuli under realistic conditions, reflected in discernible amplitude differences in response to faces and objects beyond the basic perceptual features. Conversely, the results do not provide evidence for comparable stimulus-specific perceptual processing pathways when viewing pictures of the stimuli under conventional laboratory conditions.

Research on Visual Design of Industrial Product Based on Deep Learning Algorithm

In the context of the upgrading of China's manufacturing industry, the design of industrial products is particularly important. Deep learning can be involved in automotive design activities through a variety of functions. We can use deep learning technology to design the appearance of cars through steps such as establishing a database, extracting feature values, drawing three-dimensional curves, unifying model dimensions, and analyzing errors. In this paper, the appropriate deep learning algorithm is selected through experiments to build a car image generation model based on StyleGAN2. The car image is generated by StyleGAN2 to achieve the purpose of designing car styling, and the full label database and the generation model are combined to form a car styling design tool based on deep learning, and the usability of the tool is tested through actual use. The new tool uses StyleGAN2 to generate car images for the purpose of designing car styling. The conclusion is that the function of artificial intelligence assisting designers to design automobile styling can be realized, which provides a new path for the styling research and development of other products.

Solving a classification problem using Transfer learning on small image datasets

In this study, we explore the efficiency of transfer learning in training convolutional neural networks (CNNs) for image classification tasks, focusing on a dataset with limited size. Utilizing the VGG16 model, we investigate the impact of varying the number of trainable layers on classification accuracy. Our specific classification challenge involves distinguishing between images of cars with open and closed hoods. The study demonstrates that employing transfer learning enables the pretrained VGG16 model to achieve over 97% accuracy in this binary classification task, even with a training set of just 1000 image samples. This research was conducted solely by the undersigned, who also represents the sole author of this paper.

Car make and model recognition using convolutional neural network: fine-tune AlexNet architecture

Artificial intelligence (AI) has significantly contributed to car make and model recognition in this current era of intelligent technology. By using AI, it is much easier to identify car models from any picture or video. This paper introduces a new model by fine-tuning the AlexNet architecture to determine the car model from images. First of all, our car image dataset has been created. Some of these images were taken by us, and others were taken from the website of the car connection. Then we cleaned all the unwanted images for better performance. Our dataset has ten classes containing 5,000 car images split into train and test data. After that, we augmented our data with random flip, rotation, and zoom to reduce overfitting. Finally, we used a pre-trained convolutional neural network (CNN) model AlexNet architecture. We fine-tuned AlexNet (FT-AlexNet) by adding three extra layers for better classification and compared it with the original AlexNet. To measure the performance of these models, accuracy, precision, recall, and F1-score were used. The results show that fine-tune AlexNet architecture outperforms the original AlexNet architecture. The results prove that recognition accuracy has increased due to our improvement approach.

Car Full View Dataset: Fine-Grained Predictions of Car Orientation from Images

The orientation of objects plays an important role in accurate predictions for the tasks of classification, detection, and trajectory estimation. This is especially important in the automotive domain, where estimating an accurate car orientation can significantly impact the effectiveness of the other prediction tasks. This work presents Car Full View (CFV), a novel dataset for car orientation prediction from images obtained by video recording all possible angles of individual vehicles in diverse scenarios. We developed a tool to semi-automatically annotate all the video frames with the respective car angle based on the walking speed of the recorder and manually annotated key angles. The final dataset contains over 23,000 images of individual cars along with fine-grained angle annotations. We study the performance of three state-of-the-art deep learning architectures on this dataset in three different learning settings: classification, regression, and multi-objective. The top result of 3.39° in circular mean absolute error (CMAE) shows that the model accurately predicts car orientations for unseen vehicles and images. Furthermore, we test the trained models on images from two different datasets and show their generalization capability to realistic images. We release the dataset and the best models while publishing a web service to annotate new images.

BIGNet: A Deep Learning Architecture for Brand Recognition with Geometry-Based Explainability

Abstract Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Second, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network, to learn from both scalar vector graphics’ curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens’ location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury versus economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet’s advantage of being a more explainable style-capturing agent.

Tunable four-wave mixing enabled by a self-phase modulation of chirped pulses.

We experimentally demonstrate how a concatenation of the standard and microstructure fiber segments permits adjusting the four-wave mixing sideband position over a large spectral range by varying the chirp of an input pulsed pump at a fixed wavelength in the presence of a self-phase modulation. The blue- and redshifted sidebands can stand aside over ∼200 nm and ∼450 nm from the pump, respectively, which agrees well with the numerical simulations. We validate our approach by showing the feasibility of CARS imaging.

CAR VEHICLE IMAGE OBJECT DETECTION USING YOU ONLY LIVE ONCE (YOLO)

This research aims to analyze the performance of image object detection methods using You Only Live Once (YOLO) specifically in the context of car detection. YOLO-based object detection methods have gained great attention in the artificial intelligence community due to their ability to perform real-time object detection. In this research, we focus on using YOLO to detect car objects in images. The YOLO method will be tested for performance using a dataset of car images that have been collected from various sources. This dataset includes various lighting conditions, backgrounds, and car positions. The training process will be performed using the YOLO architecture that has been pre-trained with an extensive dataset. In the testing phase, the performance of the YOLO method in car object detection will be evaluated using standard evaluation metrics such as precision, detection speed, and recall. The results of this study will show the success rate of YOLO in car detection in images and provide a better understanding of the limitations and advantages of this method. The conclusion of this research is expected to provide valuable insight into the use of the YOLO method in car object detection. This information can be used as a basis for the development and improvement of YOLO-based object detection methods, and can be applied in various applications such as automated vehicle security systems and traffic analysis.

Hybrid quantum ResNet for car classification and its hyperparameter optimization

Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters that usually require significant computational time to be adjusted. Moreover, adjustment of model hyperparameters leads to additional overhead. Because of this, new developments in machine learning models and hyperparameter optimization techniques are required. This paper presents a quantum-inspired hyperparameter optimization technique and a hybrid quantum-classical machine learning model for supervised learning. We benchmark our hyperparameter optimization method over standard black-box objective functions and observe performance improvements in the form of reduced expected run times and fitness in response to the growth in the size of the search space. We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization. Our tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.

Backward multiplex coherent anti-Stokes Raman (CARS) spectroscopic imaging with electron-multiplying CCD (EM-CCD) camera

A multiplex CARS imaging system, equipped with an EM-CCD camera, was developed to improve the sensitivity of backward CARS imaging in biological analysis using an inverted microscope. The signal-to-noise ratio was improved by a factor of ca. 3 compared to a conventional CCD mode through the use of EM gain. When imaging epithelial cells in the backward CARS configuration, intracellular organelles such as lipid droplets and nuclei were spectroscopically identified with an exposure time of only 100 ms/pixel.

RECONOCIMIENTO AUTOMÁTICO DE AUTOMÓVILES MEDIANTE MACHINE LEARNING

In this work, we perform the automatic classification of 1,000 images of five different models of automobiles. To obtain the highest precision, we have used two different classification scenarios, three algorithms, and five metrics. Also, we assume that the results can be improved by extracting the image characteristics using descriptors and using them as input. Then, we used two descriptors: a histogram of oriented gradient and a convolutional neural network ResNet-50. Our results show that the descriptors improve the classification results and obtain the highest value for the accuracy metric of 88.01 % using the ResNet-50 as a descriptor, the Training and Test Set as a scenario, and Vector Support Machine as the classification algorithm. Keywords: Convolutional Neural Networks, Gradient Oriented Histogram, Machine Learning, Fine Grain Classification, Car Images.

Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition

Human face recognition is highly accurate and exhibits a number of distinctive and well-documented behavioral "signatures" such as the use of a characteristic representational space, the disproportionate performance cost when stimuli are presented upside down, and the drop in accuracy for faces from races the participant is less familiar with. These and other phenomena have long been taken as evidence that face recognition is "special". But why does human face perception exhibit these properties in the first place? Here, we use deep convolutional neural networks (CNNs) to test the hypothesis that all of these signatures of human face perception result from optimization for the task of face recognition. Indeed, as predicted by this hypothesis, these phenomena are all found in CNNs trained on face recognition, but not in CNNs trained on object recognition, even when additionally trained to detect faces while matching the amount of face experience. To test whether these signatures are in principle specific to faces, we optimized a CNN on car discrimination and tested it on upright and inverted car images. As we found for face perception, the car-trained network showed a drop in performance for inverted vs. upright cars. Similarly, CNNs trained on inverted faces produced an inverted face inversion effect. These findings show that the behavioral signatures of human face perception reflect and are well explained as the result of optimization for the task of face recognition, and that the nature of the computations underlying this task may not be so special after all.