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}.

翻译：矿石图像的精确分割对选矿工艺的成功实施至关重要。由于矿石外观均质导致对比度低、边界不清晰，准确分割面临挑战，识别也愈发困难。本文提出一种基于多层感知器（MLP）的轻量级框架，专注于解决边缘模糊问题。具体而言，我们引入更适合高效提取低级特征的轻量级骨干网络。此外，我们设计了一个由两种MLP结构组成的特征金字塔网络，平衡局部与全局信息从而提升检测精度。进一步，我们提出一种新颖的损失函数，引导预测点与实例边缘点匹配，实现清晰的物体边界。我们开展了大量实验验证所提方法的有效性。该方法在模型体积仅73 MB的情况下，实现了超过27帧/秒（FPS）的显著处理速度。同时，在矿石图像数据集上测试时，该方法保持了一贯的高精度，在~$AP_{50}^{box}$和~$AP_{50}^{mask}$上分别取得了60.4和48.9的优异性能分数，优于当前最先进技术。源代码将在\url{https://github.com/MVME-HBUT/ORENEXT}开源。