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

• URL: http://arxiv.org/abs/2403.08499v2
• Date: Sun, 17 Nov 2024 10:58:50 GMT
• Title: Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks
• Authors: Zongqing Qi, Danqing Ma, Jingyu Xu, Ao Xiang, Hedi Qu,
• Abstract summary: This paper introduces an improved YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance the detection of foreign objects on railways and Airport runways. The dataset aims to improve the recognition capabilities of foreign object targets.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m [19.080692737423693]
  This paper proposes an improved YOLOv8m-based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid.
  arXiv  Detail & Related papers  (2025-02-11T01:58:32Z)
• EMOv2: Pushing 5M Vision Model Frontier [92.21687467702972]
  We set up the new frontier of the 5M magnitude lightweight model on various downstream tasks. Our work rethinks the lightweight infrastructure of efficient IRB and practical components in Transformer. Considering the imperceptible latency for mobile users when downloading models under 4G/5G bandwidth, we investigate the performance upper limit of lightweight models with a magnitude of 5M.
  arXiv  Detail & Related papers  (2024-12-09T17:12:22Z)
• Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an example [3.334973867478745]
  Road Damage Detection (RDD) is important for daily maintenance and safety in cities. Current UAV-based RDD research is still faces many challenges. We design a multi-scale, adaptive road damage detection model with the ability to automatically remove background interference.
  arXiv  Detail & Related papers  (2024-09-04T09:03:47Z)
• YOLO-TLA: An Efficient and Lightweight Small Object Detection Model based on YOLOv5 [19.388112026410045]
  YOLO-TLA is an advanced object detection model building on YOLOv5. We first introduce an additional detection layer for small objects in the neck network pyramid architecture. This module uses sliding window feature extraction, which effectively minimizes both computational demand and the number of parameters.
  arXiv  Detail & Related papers  (2024-02-22T05:55:17Z)
• SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector [60.42293239557962]
  We propose SpirDet, a novel approach for efficient detection of infrared small targets. We employ a new dual-branch sparse decoder to restore the feature map. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2024-02-08T05:06:14Z)
• From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution [4.107182710549721]
  We present an innovative approach that combines super-resolution and an adapted lightweight YOLOv5 architecture. Our experimental results demonstrate the model's superior performance in detecting small and densely clustered objects.
  arXiv  Detail & Related papers  (2024-01-26T05:50:58Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• HIC-YOLOv5: Improved YOLOv5 For Small Object Detection [2.4780916008623834]
  An improved YOLOv5 model: HIC-YOLOv5 is proposed to address the aforementioned problems. An involution block is adopted between the backbone and neck to increase channel information of the feature map. Our result shows that HIC-YOLOv5 has improved mAP@[.5:.95] by 6.42% and mAP@0.5 by 9.38% on VisDrone 2019-DET dataset.
  arXiv  Detail & Related papers  (2023-09-28T12:40:36Z)
• Robust Trajectory Prediction against Adversarial Attacks [84.10405251683713]
  Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving systems. These methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks.
  arXiv  Detail & Related papers  (2022-07-29T22:35:05Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.