RESUMEN
Object detection in high resolution enables the identification and localization of objects for monitoring critical areas with precision. Although there have been improvements in object detection at high resolution, the variety of object scales, as well as the diversity of backgrounds and textures in high-resolution images, make it challenging for detectors to generalize successfully. This study introduces a new method for object detection in high-resolution images. The pre-processing stage of the method includes ISA and SAM to slice the input image and segment the objects in bounding boxes, respectively. In order to improve the resolution in the slices, the first layer of YOLO is designed as SRGAN. Thus, before applying YOLO detection, the resolution of the sliced images is increased to improve features. The proposed system is evaluated on xView and VisDrone datasets for object detection algorithms in satellite and aerial imagery contexts. The success of the algorithm is presented in four different YOLO architectures integrated with SRGAN. According to comparative evaluations, the proposed system with Yolov5 and Yolov8 produces the best results on xView and VisDrone datasets, respectively. Based on the comparisons with the literature, our proposed system produces better results.
RESUMEN
AIM: This study was conducted to determine the segmentation, classification, object detection, and accuracy of skin burn images using artificial intelligence and a mobile application. With this study, individuals were able to determine the degree of burns and see how to intervene through the mobile application. METHODS: This research was conducted between 26.10.2021-01.09.2023. In this study, the dataset was handled in two stages. In the first stage, the open-access dataset was taken from https://universe.roboflow.com/, and the burn images dataset was created. In the second stage, in order to determine the accuracy of the developed system and artificial intelligence model, the patients admitted to the hospital were identified with our own design Burn Wound Detection Android application. RESULTS: In our study, YOLO V7 architecture was used for segmentation, classification, and object detection. There are 21018 data in this study, and 80% of them are used as training data, and 20% of them are used as test data. The YOLO V7 model achieved a success rate of 75.12% on the test data. The Burn Wound Detection Android mobile application that we developed in the study was used to accurately detect images of individuals. CONCLUSION: In this study, skin burn images were segmented, classified, object detected, and a mobile application was developed using artificial intelligence. First aid is crucial in burn cases, and it is an important development for public health that people living in the periphery can quickly determine the degree of burn through the mobile application and provide first aid according to the instructions of the mobile application.