# Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks

arXiv: https://arxiv.org/abs/2403.08499 · HTML: https://mlsanity.com/paper/2403.08499/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks](https://mlsanity.com/paper/2303.03667/) (2023)
- [FOD-A: A Dataset for Foreign Object Debris in Airports](https://mlsanity.com/paper/2110.03072/) (2021)

## Built on by (most cited first, 4 of 4)

- [YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving](https://mlsanity.com/paper/2409.03320/) (2024)
- [A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model](https://mlsanity.com/paper/2407.08942/) (2024)
- [Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism](https://mlsanity.com/paper/2409.08588/) (2024)
- [Detecting and Classifying Defective Products in Images Using YOLO](https://mlsanity.com/paper/2412.16935/) (2024)

## Abstract (arXiv)

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.