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Xujun Li

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EAAI Journal 2026 Journal Article

Lightweight method of foreign matter detection in coal conveying based on improved you only look once version 8 and embedded equipment

  • Guanfeng Du
  • Hongzheng Zhang
  • Yupeng Luo
  • Zhibo Bao
  • Zhiwei Li
  • Mingxin Zhou
  • Zhelin Liu
  • Shengxian Cao

During the process of conveying pulverized coal, the mixed foreign matter will not only affect the combustion efficiency of pulverized coal, but also cause safety accidents in coal conveying equipment. Therefore, it is very important to monitor the foreign matter in the process of conveying coal. Due to the limited scope of the actual conveying site, embedded equipment is needed for inspection. Aiming at the computing and memory challenges of embedded equipment, an improved lightweight YOLOv8 (you only look once version 8) algorithm is proposed. In the backbone of the algorithm, cross stage partial with 2 convolutions and lightweight PoolFormer (C2f_LPF) module is used to extract lightweight features, and foreign matter information is extracted by using multi-scale concerns in cross stage partial with deformable convolution (CSPDC) module. Then the part of the feature aggregation (PFA) module of the neck is used for lightweight feature fusion. The proposed C2f_LPF+CSPDC+PFA combination realizes a more balanced optimization of lightweight performance and detection accuracy and provides a solution to the contradiction between the limitation of computing resources of embedded equipment and the demand for real-time detection of accuracy in coal conveying. A self-made datasets contain scrap iron, stones, wooden stick and branch are trained and compared with faster region-based convolutional neural network (Faster R-CNN) and YOLO (you only look once) series algorithm on computer and embedded equipment. The datasets consist of 612 images with 4413 examples of foreign matter, which are collected on a lab-scale self-made coal conveying platform. The mean average precision (mAP), Giga floating-point operations per second (GFLOPS), parameters and frame per second (FPS) on embedded equipment are 0. 963, 6. 1, 2. 44 million and 37. 04, respectively. Compared with the original YOLOv8, the computation and parameters are reduced by 24. 7 % and 18. 9 % respectively, and the FPS is improved by 29. 6 %. By contrast, it also has better results than other algorithms, which is well compatible with the configuration requirements of embedded equipment and achieves a good balance between precision and speed. This shows a promising performance on a lab-scale platform and may be extended to real industrial lines after further validation.

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ICLR Conference 2025 Conference Paper

MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science

  • Erle Zhu
  • Yadi Liu
  • Zhe Zhang
  • Xujun Li
  • Jin Zhou
  • Xinjie Yu
  • Minlie Huang
  • Hongning Wang

Pre-trained on extensive text and image corpora, current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks. However, their performance is still lacking in physical domains that require understanding diagrams with complex physical structures and quantitative analysis based on multi-modal information. To address this, we develop a new framework, named **M**ulti-Modal Scientific Re**A**soning with **P**hysics Perception and **S**imulation (**MAPS**) based on an MLLM. MAPS decomposes expert-level multi-modal reasoning task into physical diagram understanding via a Physical Perception Model (PPM) and reasoning with physical knowledge via a simulator. The PPM module is obtained by fine-tuning a visual language model using carefully designed synthetic data with paired physical diagrams and corresponding simulation language descriptions. At the inference stage, MAPS integrates the simulation language description of the input diagram provided by PPM and results obtained through a Chain-of-Simulation process with MLLM to derive the underlying rationale and the final answer. Validated using our collected college-level circuit analysis problems, MAPS significantly improves reasoning accuracy of MLLM and outperforms all existing models. The results confirm MAPS offers a promising direction for enhancing multi-modal scientific reasoning ability of MLLMs. We will release our code, model and dataset used for our experiments upon publishing of this paper.

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v2026.09.13