Glass-biscuit Computer Vision Project

Image segmentation using YOLOv8 Model, Computer vision algorithms have transformed how we analyze images and videos. Object detection, pivotal in computer vision, has advanced greatly due to models like YOLOv8 and Mask R-CNN. Both have enhanced real-time detection and accuracy, benefiting many fields.

• YOLOv8 and Mask R-CNN are at the forefront of object detection in computer vision.
• YOLOv8 shines in real-time detection, ideal for applications with time constraints.
• Mask R-CNN is exceptional in instance segmentation, accurately delineating object boundaries.
• The discussion will center on metrics like Mean Average Precision (MAP) and detection scores.
• Their distinct architectures significantly affect how well they detect and segment objects

YOLOv8 is best combo with accuracy and speed, in this project we have demostrated image segmentation task, which we performed using YOLOv8 model , and got outstanding results, this model can be used in robot-picking tasks, to aid the robotic- arm. Images were captured through INTEL Realsense camera, attached on robotic arm. Further research can be done, on image segmentation of images which contains clustered objects, and transparent objects to aid the robotic arm differentating between the objects.