A New Approach for Super Resolution Object Detection Using an Image Slicing Algorithm and the Segment Anything Model
Künye
Muhammed Telçeken, Devrim Akgun, Sezgin Kacar, & Bunyamin Bingol. (2024). A New Approach for Super Resolution Object Detection Using an Image Slicing Algorithm and the Segment Anything Model. Sensors, 24(14), 4526–4526. https://doi.org/10.3390/s24144526 Özet
: 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.