  The precise segmentation of ore images is critical to the successful
execution of the beneficiation process. Due to the homogeneous appearance of
the ores, which leads to low contrast and unclear boundaries, accurate
segmentation becomes challenging, and recognition becomes problematic. This
paper proposes a lightweight framework based on Multi-Layer Perceptron (MLP),
which focuses on solving the problem of edge burring. Specifically, we
introduce a lightweight backbone better suited for efficiently extracting
low-level features. Besides, we design a feature pyramid network consisting of
two MLP structures that balance local and global information thus enhancing
detection accuracy. Furthermore, we propose a novel loss function that guides
the prediction points to match the instance edge points to achieve clear object
boundaries. We have conducted extensive experiments to validate the efficacy of
our proposed method. Our approach achieves a remarkable processing speed of
over 27 frames per second (FPS) with a model size of only 73 MB. Moreover, our
method delivers a consistently high level of accuracy, with impressive
performance scores of 60.4 and 48.9 in~$AP_{50}^{box}$ and~$AP_{50}^{mask}$
respectively, as compared to the currently available state-of-the-art
techniques, when tested on the ore image dataset. The source code will be
released at \url{https://github.com/MVME-HBUT/ORENEXT}.
