Autonomous driving technologies are rapidly advancing, driven by the need for safer, more efficient, and intelligent transportation systems. A fundamental component of these systems is the perception module, which enables vehicles to understand and react to their surrounding environment. Object detection, in particular, is essential for identifying dynamic and static elements on the road, such as pedestrians, vehicles, traffic signs, and obstacles. In this work, we explore and enhance the capabilities of state-of-the-art deep learning-based object detectors within the YOLO (You Only Look Once) family, focusing on the latest versions: YOLOv10 and YOLOv11. We fine-tuned and optimized multiple variants of each model—namely, nano (n), small (s), medium (m), and large (l)—to improve detection accuracy and computational efficiency for real-time autonomous driving applications. The models were trained and evaluated on a diverse road object, and performance was measured using key metrics including precision, mean Average Precision (mAP), and Precision-Recall curves. Experimental results reveal that the fine-tuned YOLOv10n achieved a peak class-level precision of 1.00 at a specific confidence threshold (0.997), indicating that perfect precision was observed for certain classes under high-confidence conditions, while the overall mean precision and mAP metrics reflect more balanced model performance, while YOLOv11s attained the best result within its group with a precision of 0.91 at a threshold of 0.972. These findings demonstrate the potential of tailored YOLO architectures to meet the demanding requirements of real-world autonomous navigation systems.

This study proposes a new approach for the Facial Expression Recognition (FER) system that combines the Quantum Gazelle Optimization Algorithm (QGOA), local binary patterns (LBP), and Histograms of Oriented Gradients (HOG) with an optimized Deep Neural Network (DNN) classifier. The system uses computer vision techniques and deep learning algorithms to identify emotions in facial expressions. The proposed technique first employs the HOG and LBP descriptors, crucial components with excellent pattern recognition capabilities. These descriptors provide features resilient to small local changes in posture and lighting. However, they also generate unimportant and obtrusive characteristics that hinder classification performance. The proposed approach uses a wrapper-based feature selector called QGOA to solve this issue, which decreases the training complexity and improves recognition performance. QGOA takes advantage of the properties of quantum computing to regulate the diversity of face features and make proper selections using quantum measurements and Q-bit superstitious states. Finally, the optimized DNN detects facial emotions based on the selected features. The proposed approach was tested on the widely adopted FER2013 dataset. The results of the extensive analysis demonstrate the effectiveness of the proposed approach over state-of-the-art systems.

Summary This article puts forward a fast chaos‐RSA‐based hybrid cryptosystem to secure and authenticate secret images. The SHA‐512 is used to generate a 512‐bit initial key. The RSA system is used to encrypt the initial secret key and signature generation for both the sender and image authentication. In fact, a powerful block‐cipher algorithm is developed to encrypt and decrypt images with a high level of security. At this stage, a strong PRNG based on four chaotic systems is propounded to generate high‐quality keys. Therefore, an improved architecture is suggested. It performs confusion and diffusion of images with low computational complexity. In the final step, the encrypted secret key, signature, and encrypted image are combined together in order to obtain an encrypted signed image. The block‐cipher algorithm is evaluated in‐depth for several ordinary and medical images with different types, content, and size. The obtained simulation results demonstrate that the system enables high‐level security. The entropy has achieved a value of 7.9998 which is the most important feature of randomness. A comparative study against numerous recent encryption algorithms demonstrates that the proposed algorithm provides good results.

In this paper we propose a novel and efficient hardware implementation of an image watermarking system based on the Haar Discrete Wavelet Transform (DWT). DWT is used in image watermarking to hide secret pieces of information into a digital content with a good robustness. The main advantage of Haar DWT is the frequencies separation into four subbands (LL, LH, HL, and HH) which can be treated independently. This permits ensuring a better compromise between robustness and visibility factors. A Field Programmable Gate Array (FPGA) that is based on a very large scale integration architecture of the watermarking algorithm is developed to accelerate media authentication. A hardware cosimulation strategy using the Matlab-Xilinx system generator (XSG) was applied to prove the validity of the suggested implementation. The hardware cosimulation results show the effectiveness of the developed architecture in terms of visibility and robustness against several attacks. The proposed hardware system presents also a high performance in terms of the operating speed.

This study presents a novel chaotic–neural network of image encryption and decryption image applied to the domain of medical. The main objective behind the proposed technique is to ensure the safety of medical images with a less complex algorithm compared with the existing methods. In order to improve the robustness, the totality of the pixels related to the host image is XORed with a generation key. After that, with a chaotic system (logistic map), the binary sequence is generated in order to set the weights w ij and bias bi of neuron network with the goal of encrypting the pixels issued from the previous step. Simulation and experiments were carried out on medical images coded on 8 and 12 bits/pixel. The obtained results confirmed the performance and the efficiency of the proposed method, which is compliant with Digital Imaging and Communications in Medicine standards.