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This document is a correction notice for an article titled "Parking Lot Occupancy Detection with Improved MobileNetV3" published in the journal Sensors. The correction includes errors in figures and figure legends, as well as text corrections in various sections of the article. The article discusses different approaches to parking lot occupancy detection, including traditional machine learning approaches and deep learning approaches. It also mentions several studies that have proposed methods using feature extraction and machine learning methodologies. The article compares different deep neural networks and datasets used for parking occupancy classification. The correction notice provides updated figures and corrects errors in the text. This document is a correction to a previous article. It provides a revised list of references for the article, ensuring that the correct sources are cited. The correction does not affect the scientific conclusions of the original article. The document also includes three figures: Figure 6 shows a graph of empty and busy parking spaces, Figure 7 displays samples from the CNRPark-EXT dataset taken in different weather conditions, and Figure 8 shows examples of different parking lots and weather conditions from the PKLot dataset. The authors of the correction are Yusufbek Yuldashev, Mukhriddin Mukhiddinov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov, and Jinsoo Cho. [Extracted from the article]

It is no secret today that quality software has a higher superiority than leading technology solutions in computer vision. Remarkable advancement has been achieved in ultrasound image classification, essentially because of the availability of large-scale annotated datasets and deep convolutional neural networks (CNN). Applying CNN in the sphere of medicine is also becoming an active and attractive research area for researchers. In this paper, we introduce an efficient method for the classification of fetal ultrasound images using CNN. To classify these images, we collected four types of fetal ultrasound images from hospitals and internet sources. We first analyze and evaluate various CNN models such as AlexNet, Inception_v3, and MobileNet_v1 for training and testing. Then, the results of these CNN models are quantitatively compared with the proposed model in accuracy and speed. The results show that the proposed classification method can be recognized faster without compromising performance and adjust the ultrasound image parameters quickly and automatically. The proposed CNN model's weight size is less than 1 Mb and can be used on mobile or embedded operating systems. We also developed and tested the application on the Android operating system-based mobile device. [ABSTRACT FROM AUTHOR]

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved. [ABSTRACT FROM AUTHOR]