Thanks to the rapid development of computer vision and deep learning technologies, advanced driver assistance systems (ADAS) have recently become widespread. These systems aim to increase driving safety and reduce the number of traffic accidents. Modern cars usually have ADAS systems integrated into their electronics, but other vehicles do not have such an integrated system. This paper presents a portable and image-based ADAS system for real-time detection of traffic signs, vehicles, and pedestrians. To realize real-time detection, the developed system uses the YOLO v5 algorithm. This single-stage detector is very popular as it has high detection speed and accuracy. The model was trained on the Tesla P100 graphics processing unit (GPU) with nearly 2500 images and 8 hours using GTSRB and study-specific dataset to analyze the developed system. Then, the implementation metrics (F1 score, P, R, PR curves) were calculated to evaluate the training and testing performances of the model. In addition, the model was compared in low-power, high-performance embedded platforms and in a computer to measure the real-time performance. The comparison results for (Jetson Xavier AGX, Jetson Xavier Nx, Jetson Nano and Test PC) achieved a speed of (43.59, 23.17, 6.4 and 3.41) frames per second for real-time detection, respectively. Considering the excellent accuracy and high speed, this study will guide researchers in demonstrating the efficiency and suitability of real-time road object detection with YOLO v5 on mobile platforms.

Traffic signs and road objects detection is significant issue for driver safety. It has become popular with the development of autonomous vehicles and driver-assistant systems. This study presents a real-time system that detects traffic signs and various objects in the driving environment with a camera. Faster R-CNN architecture was used as a detection method in this study. This architecture is a well-known two-stage approach for object detection. Dataset was created by collecting various images for training and testing of the model. The dataset consists of 1880 images containing traffic signs and objects collected from Turkey with the GTSRB dataset. These images were combined and divided into the training set and testing set with the ratio of 80/20. The model's training was carried out in the computer environment for 8.5 hours and approximately 10000 iterations. Experimental results show the real-time performance of Faster R-CNN for robustly traffic signs and objects detection.

The production of electric and hybrid vehicles on land and at sea is now widely used to reduce carbon emissions. While charging the batteries of electric vehicles can be done manually, studies to automate this process are increasing. Recently, many studies based on computer vision have been carried out to provide real-time and more accurate detection of charging systems. For automatic charging, the position and distance of the socket on the ship approaching the shore can be determined by processing the image taken by the camera. For this purpose, in this study, an interface has been developed for the detection system of electrical charging system sockets by using classical image processing and the YOLO technique, which is one of the deep learning methods. With the developed interface, the socket's position can be detected and monitored in real-time through the image taken from the camera. Thus, automatic charging can be performed successfully.

In the above article [1] , two references [2] , [3] were missing. The first sentence of Section 3.1 should read “The datasets of this work include various class images from the dataset used in [2] , our dataset [1] and [3] , and GTSRB datasets.” The authors requested this correction to give detailed information about the Dataset section.

With the gradual development of Industry 4.0, the internet of things (IoT) concept has become an even more current and fundamental study topic. Consisting of devices and objects with communication capability, IoT is a network that uses internet infrastructure, especially for data collection, display, decision-making, control, and optimization of processes. Recently, patient tracking systems have become even more critical with Covid19 and have diversified in health for IoT topics such as biomedical device tracking and disease diagnosis. Within the scope of this study, a prototype of a patient tracking system was developed over the sensor in order to contribute to the biomedical field. We aimed to observe real-time heart rates using WebSockets to demonstrate its use in the medical field via the web application. Monitoring the heart rate using a WebSocket can help doctors make faster and better diagnoses. The current technology study instantly collected the patient's heart rhythm with the pulse sensor. The pulse data collected in real time was then transferred to a web platform with the NodeMCU ESP 8266 board. With this platform, the patient was monitoring in real-time. With the opportunities provided by the study, the doctor implemented an application monitors the instantaneous pulse of the patients.