# An Intelligent Obstacle Detection Method for Rail Transit Scenarios

## TL;DR

This paper introduces ACX-YOLOv8, an improved object detection method for identifying obstacles on railway tracks, offering better accuracy and efficiency for real-time monitoring.

## Contribution

The novel ACX-YOLOv8 integrates SCAM, CDConv, and an X6 detection head to enhance obstacle detection in railway environments.

## Key findings

- ACX-YOLOv8 achieves 87.1% mAP50 on the test dataset, a 2.7% improvement over the baseline YOLOv8.
- The model has 4.85 million parameters and maintains lightweight performance while ensuring detection precision.
- It shows a 1.8% mAP50 improvement on the PASCAL VOC dataset, demonstrating strong generalization ability.

## Abstract

Traditional signal equipment is incapable of real-time monitoring of foreign objects intruding into track zones. To effectively ensure the operational safety of trains, this paper presents an intelligent obstacle detection approach of visual sensing for railway track regions based on YOLOv8, named ACX-YOLOv8. Built upon the baseline YOLOv8 framework, the proposed method first incorporates the spatial coordinate attention mechanism (SCAM) to enhance the model’s ability to capture long-range dependencies and local fine-grained details, thereby improving its perceptual capacity and feature representation performance. Subsequently, the cascaded dilated convolution (CDConv) module is integrated to effectively extract multi-scale image features, strengthening the model’s capability to identify foreign objects in complex railway environments. Finally, an X6 decoupled detection head is devised to further elevate the model’s detection accuracy and inference efficiency. Field experiments in real-world scenarios are conducted to validate the effectiveness of the improved algorithm. Experimental results demonstrate that the optimized ACX-YOLOv8 model has a total parameter count of 4.85 million and achieves a mean average precision at IoU = 0.5 (mAP50) of 87.1% on the test dataset, which is a 2.7% improvement over the original YOLOv8 baseline model. The lightweight property and detection precision of the model are both effectively guaranteed. Furthermore, to verify the generalization ability of the algorithm, tests are performed on the public PASCAL VOC dataset, where the mAP50 value is increased by 1.8%. These findings indicate that the ACX-YOLOv8 algorithm can detect various foreign objects invading railway track areas rapidly and accurately. It provides efficient and reliable technical support for real-time obstacle monitoring in complex and variable railway track environments and contributes to enhancing the safety and intelligentization level of railway operations.