In recent years, there have been frequent incidents of foreign objects
intruding into railway and Airport runways. These objects can include
pedestrians, vehicles, animals, and debris. This paper introduces an improved
YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance
the detection of foreign objects on railways and Airport runways. This study
proposes a new dataset, AARFOD (Aero and Rail Foreign Object Detection), which
combines two public datasets for detecting foreign objects in aviation and
railway systems. The dataset aims to improve the recognition capabilities of
foreign object targets. Experimental results on this large dataset have
demonstrated significant performance improvements of the proposed model over
the baseline YOLOv5 model, reducing computational requirements. improved YOLO
model shows a significant improvement in precision by 1.2%, recall rate by
1.0%, and mAP@.5 by 0.6%, while mAP@.5-.95 remained unchanged. The parameters
were reduced by approximately 25.12%, and GFLOPs were reduced by about 10.63%.
In the ablation experiment, it is found that the FasterNet module can
significantly reduce the number of parameters of the model, and the reference
of the attention mechanism can slow down the performance loss caused by
lightweight.

利用 FasterNet 和注意机制改进了 YOLOv5 架构，提高了对火车道和机场跑道上外来物体的检测能力，通过新数据集 AARFOD（航空铁路外来物体检测）证明了该模型相对于基准 YOLOv5 模型在性能上的显著提升，并降低了计算需求。

基于 Transformer 和 Tensor Product 的学生情感识别的多模态融合网络

A Multimodal Fusion Network For Student Emotion Recognition Based on  Transformer and Tensor Product

Reliable obstacle detection on railways could help prevent collisions that
result in injuries and potentially damage or derail the train. Unfortunately,
generic object detectors do not have enough classes to account for all possible
scenarios, and datasets featuring objects on railways are challenging to
obtain. We propose utilizing a shallow network to learn railway segmentation
from normal railway images. The limited receptive field of the network prevents
overconfident predictions and allows the network to focus on the locally very
distinct and repetitive patterns of the railway environment. Additionally, we
explore the controlled inclusion of global information by learning to
hallucinate obstacle-free images. We evaluate our method on a custom dataset
featuring railway images with artificially augmented obstacles. Our proposed
method outperforms other learning-based baseline methods.

铁路上可靠的障碍物检测可以帮助防止导致伤害、潜在的损坏或脱轨的碰撞。我们提出利用浅层网络从正常的铁路图像中学习铁路分割，并通过学习虚构无障碍图像的全局信息的受控包含来改进方法。我们在一个包含人工增强障碍物的铁路图像数据集上评估我们的方法，结果表明我们的方法优于其他基于学习的基准方法。