Emotions are an integral part of human interactions and are significant factors in determining user satisfaction or customer opinion. speech emotion recognition (SER) modules also play an important role in the development of human–computer interaction (HCI) applications. A tremendous number of SER systems have been developed over the last decades. Attention-based deep neural networks (DNNs) have been shown as suitable tools for mining information that is unevenly time distributed in multimedia content. The attention mechanism has been recently incorporated in DNN architectures to emphasise also emotional salient information. This paper provides a review of the recent development in SER and also examines the impact of various attention mechanisms on SER performance. Overall comparison of the system accuracies is performed on a widely used IEMOCAP benchmark database.

Road discrepancies such as potholes and road cracks are often present in our day-to-day commuting and travel. The cost of damage repairs caused by potholes has always been a concern for owners of any type of vehicle. Thus, an early detection processes can contribute to the swift response of road maintenance services and the prevention of pothole related accidents. In this paper, automatic detection of potholes is performed using the computer vision model library, You Look Only Once version 3, also known as Yolo v3. Light and weather during driving naturally affect our ability to observe road damage. Such adverse conditions also negatively influence the performance of visual object detectors. The aim of this work was to examine the effect adverse conditions have on pothole detection. The basic design of this study is therefore composed of two main parts: (1) dataset creation and data processing, and (2) dataset experiments using Yolo v3. Additionally, Sparse R-CNN was incorporated into our experiments. For this purpose, a dataset consisting of subsets of images recorded under different light and weather was developed. To the best of our knowledge, there exists no detailed analysis of pothole detection performance under adverse conditions. Despite the existence of newer libraries, Yolo v3 is still a competitive architecture that provides good results with lower hardware requirements.

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Potholes pose a significant problem for road safety and infrastructure. They can cause damage to vehicles and present a risk to pedestrians and cyclists. The ability to detect potholes in real time and with a high level of accuracy, especially under different lighting conditions, is crucial for the safety of road transport participants and the timely repair of these hazards. With the increasing availability of cameras on vehicles and smartphones, there is a growing interest in using computer vision techniques for this task. Convolutional neural networks (CNNs) have shown great potential for object detection tasks, including pothole detection. This study provides an overview of computer vision algorithms used for pothole detection. Experimental results are then used to evaluate the performance of the latest CNN-based models for pothole detection in different real-world road conditions, including rain, sunset, evening, and night, as well as clean conditions. The models evaluated in this study include both conventional and the newest architectures from the region-based CNN (R-CNN) and You Only Look Once (YOLO) families. The YOLO models demonstrated a faster detection response and higher accuracy in detecting potholes under clear, rain, sunset, and evening conditions. R-CNN models, on the other hand, performed better in the worse-visibility conditions at night. This study provides valuable insights into the performance of different CNN models for pothole detection in real road conditions and may assist in the selection of the most appropriate model for a specific application.

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