ArticleSensors (Basel, Switzerland)2022
Improvement of Lightweight Convolutional Neural Network Model Based on YOLO Algorithm and Its Research in Pavement Defect Detection.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A lightweight YOLO11n seg framework for real time surface crack detection with segmentation.Scientific reports · 2026Article
- YOLO11-WLBS: an efficient model for pavement defect detection.Scientific reports · 2026Article
- An improved YOLO11 UAV toy models target detection model.Scientific reports · 2025Article
- YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance.Sensors (Basel, Switzerland) · 2025Article
- MGL-YOLO: A Lightweight Barcode Target Detection Algorithm.Sensors (Basel, Switzerland) · 2024Article
- YOLO-DRS: A Bioinspired Object Detection Algorithm for Remote Sensing Images Incorporating a Multi-Scale Efficient Lightweight Attention Mechanism.Biomimetics (Basel, Switzerland) · 2023Article
- Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7.Sensors (Basel, Switzerland) · 2023Article
- Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Micro-Leakage Image Recognition Method for Internal Detection in Small, Buried Gas Pipelines.Sensors (Basel, Switzerland) · 2023Article
- Computer Vision Based Pothole Detection under Challenging Conditions.Sensors (Basel, Switzerland) · 2022Article
- RD-YOLO: An Effective and Efficient Object Detector for Roadside Perception System.Sensors (Basel, Switzerland) · 2022Article
- NRT-YOLO: Improved YOLOv5 Based on Nested Residual Transformer for Tiny Remote Sensing Object Detection.Sensors (Basel, Switzerland) · 2022Article
- Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD).Sensors (Basel, Switzerland) · 2022Article
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2 authors.
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Abstract
To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.
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