try this:

https://ieeexplore.ieee.org/abstract/document/8367193

Dear Parsa Sarosh

YOLO is a clever convolutional neural network (CNN) for doing object detection in real-time. With YOLO, a single CNN simultaneously predicts multiple bounding boxes and class probabilities for those boxes. YOLO trains on full images and directly optimizes detection performance. YOLO algorithm is an algorithm based on regression, instead of selecting the interesting part of an Image, it predicts classes and bounding boxes for the whole image in one run of the Algorithm. Ultimately, we aim to predict a class of an object and the bounding box specifying object location. Image segmentation is the task of clustering parts of an image together that belong to the same object class. This process is also called pixel-level classification. In other words, it involves partitioning images (or video frames) into multiple segments or objects.

Also, kindly check these papers:

https://www.diva-portal.org/smash/get/diva2:1421305/FULLTEXT01.pdf

https://towardsdatascience.com/yolo-you-only-look-once-3dbdbb608ec4

https://arxiv.org/pdf/1807.05511

Thank you very much

Please refer to the following papers:

Article Skin Lesion Segmentation in Dermoscopic Images with Combinat...

Conference Paper ViT-YOLO:Transformer-Based YOLO for Object Detection

Yes, sometimes it is useful