The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric. Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection. Moreover, the approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and error-prone processes. Hence, an automated system is needed for classification of woven fabric to improve productivity. In this paper, we propose a deep learning model based on data augmentation and transfer learning approach for the classification and recognition of woven fabrics. The model uses the residual network (ResNet), where the fabric texture features are extracted and classified automatically in an end-to-end fashion. We evaluated the results of our model using evaluation metrics such as accuracy, balanced accuracy, and F1-score. The experimental results show that the proposed model is robust and achieves state-of-the-art accuracy even when the physical properties of the fabric are changed. We compared our results with other baseline approaches and a pretrained VGGNet deep learning model which showed that the proposed method achieved higher accuracy when rotational orientations in fabric and proper lighting effects were considered.

Traffic congestion control is becoming a popular field of research due to the rapid development of the automotive market. Vehicular ad hoc networks (VANETs) have become the core research technology for numerous application possibilities related to road safety. Road congestions have been a serious issue of all time since the nodes have high mobility and transmission range is limited, resulting in an interruption of communication. One of the significant technical challenges faced in implementing VANET is the design of the routing protocol, providing adequate information and a reliable source for the destination. We proposed a novel mechanism unmanned aerial vehicle (UAV)-assisted ad hoc on-demand distance vector (AODV) routing protocol (UAVa) for current-time traffic information accumulation and sharing to the entire traffic network and to control congestions before it happens. The UAV-assisted (UAVa) protocol is dedicated to urban environments, and its primary goal is to enhance the performance of routing protocols based on intersections. We compared the overall performance of existing routing protocols, namely ad hoc on-demand distance vector. The simulations were done by using OpenStreetMap (OSM), Network Simulator (NS-2.35), Simulation of Urban Mobility (SUMO), and VanetMobiSim. Furthermore, we compared the simulation results with AODV, and it shows that UAV-assisted (UAVa) AODV has significantly enhanced the packet delivery ratio, reduced the end-to-end delay, improved the average and instant throughput, and saved more energy. The results show that the UAVa is more robust and effective and we can conclude that UAVa is more suitable for VANETs.

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and F1 − Score. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.

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The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications (e.g., surveillance and human-computer interaction [HCI]). Images of varying levels of illumination, occlusion, and other factors are captured in uncontrolled environments. Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances, and faces may be covered by a scarf, hijab, or mask due to the COVID-19 pandemic. The periocular region is a reliable source of information because it features rich discriminative biometric features. However, most existing gender classification approaches have been designed based on hand-engineered features or validated in controlled environments. Motivated by the superior performance of deep learning, we proposed a new method, PeriGender, inspired by the design principles of the ResNet and DenseNet models, that can classify gender using features from the periocular region. The proposed system utilizes a dense concept in a residual model. Through skip connections, it reuses features on different scales to strengthen discriminative features. Evaluations of the proposed system on challenging datasets indicated that it outperformed state-of-the-art methods. It achieved 87.37%, 94.90%, 94.14%, 99.14%, and 95.17% accuracy on the GROUPS, UFPR-Periocular, Ethnic-Ocular, IMP, and UBIPr datasets, respectively, in the openworld (OW) protocol. It further achieved 97.57% and 93.20% accuracy for adult periocular images from the GROUPS dataset in the closed-world (CW) and OW protocols, respectively. The results showed that the middle region between the eyes plays a crucial role in the recognition of masculine features, and feminine features can be identified through the eyebrow, upper eyelids, and corners of the eyes. Furthermore, using a whole region without cropping enhances PeriGender's learning capability, improving its understanding of both eyes' global structure without discontinuity.