Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of expensive, high-quality vision sensors is uneconomical in most cases; thus, images are initially captured and then translated from low resolution to high resolution. For this purpose, several traditional techniques such as bilinear, bicubic, super-resolution convolutional neural network, and super-resolution generative adversarial network (SRGAN) have been developed over time to upgrade low-quality images. However, most studies in this area pertain to the conversion of low-resolution images to super-resolution images, and little attention has been paid to motion de-blurring. This work extends SRGAN by adding an intelligent motion-deblurring method (termed SRGAN-LP), which helps to enhance the image resolution and remove motion blur from the given images. A comprehensive and new domain-specific dataset was developed to achieve improved results. Moreover, maintaining higher quantitative and qualitative results in comparison to the ground truth images, this study upscales the provided low-resolution image four times and removes the motion blur to a reasonable extent, making it suitable for surveillance applications.

Trajectory tracking and robust motion control remain central challenges in autonomous robotic systems operating under nonlinear and uncertain conditions. This study proposes a novel hybrid control framework that integrates Integral Sliding Mode Control (ISMC), whose control parameters are calculated using a Sugeno-type Fuzzy Logic Controller (FLC) and a Modified Elephant Herding Optimization (MEHO) algorithm. The approach is implemented on a three-wheeled omnidirectional mobile robot (TOMR), with detailed dynamic and kinematic modeling. The MEHO algorithm enhances exploration–exploitation balance and accelerates convergence through adaptive update mechanisms. To assess the performance of the proposed controller, a triangle and C-shape trajectories are implemented. Simulation results using MATLAB R2022b show that the proposed system reduces positional errors in the X and Y axes to below 0.005 m, and orientation error to 0.0014 rad within 2 s. The root mean square errors (RMSE) for X, Y, and orientation for the triangle trajectory are 3.48 × 10⁻⁵ m, 2.51 × 10⁻⁶ m, and 0.00287 rad, respectively and for the C-shape trajectory are 6.538*10^− 6 m, 2.614*10^− 6 m, and 1.051*10^− 5 rad for X, Y, and orientation, respectively. Compared to classical EHO-based and adaptive neural sliding controllers, the proposed method achieves up to 50% lower torque variation and over 60% faster settling time. The modular and learning-based design enables generalization to other robotic platforms, including aerial robots operating in uncertain or dynamic environments.

Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world road accidents due to the conditions of the drivers’ state such as drowsiness, stress, tiredness, and age during driving. In such cases, driver fatigue detection is mandatory to avoid road accidents and ensure a comfortable journey. To date, several complex systems have been proposed that have problems due to practicing hand feature engineering tools, causing lower performance and high computation. To tackle these issues, we propose an efficient deep learning-assisted intelligent fatigue and age detection system (FADS) to detect and identify different states of the driver. For this purpose, we investigated several neural computing-based methods and selected the most appropriate model considering its feasibility over edge devices for smart surveillance. Next, we developed a custom convolutional neural network-based system that is efficient for drowsiness detection where the drowsiness information is fused with age information to reach the desired output. The conducted experiments on the custom and publicly available datasets confirm the superiority of the proposed system over state-of-the-art techniques.