YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes

Khare, Om; Gandhi, Shubham; Rahalkar, Aditya; Mane, Sunil

doi:10.1109/punecon58714.2023.10449999

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Data-set class-balancing and the Convolutional Vision Transformer

Escobar-Ortiz,

Amezquita-Dussan,

Galindo-Sanchez

et al. 2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Data-set class-balancing and the Convolutional Vision Transformer

Escobar-Ortiz,

Amezquita-Dussan,

Galindo-Sanchez

et al. 2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Image Inpainting using YOLOv8 and LaMa Model

Jain,

Shivam,

Bidargaddi

et al. 2024

2024 5th International Conference for Emerging Technology (INCET)

View full text Add to dashboard Cite

SA3Det: Detecting Rotated Objects via Pixel-Level Attention and Adaptive Labels Assignment

Wang,

Cai,

Luo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Remote sensing of rotated objects often encounters numerous small and dense objects. To tackle small-object neglect and inaccurate angle predictions in elongated objects, we propose SA3Det, a novel method employing Pixel-Level Attention and Adaptive Labels Assignment. First, we introduce a self-attention module that learns dense pixel-level relations between features extracted by the backbone and neck, effectively preserving and exploring the spatial relationships of potential small objects. We then introduce an adaptive label assignment strategy that refines proposals by assigning labels based on loss, enhancing sample selection during training. Additionally, we designed an angle-sensitive module that enhances angle prediction by learning rotational feature maps and incorporating multi-angle features. These modules significantly enhance detection accuracy and yield high-quality region proposals. Our approach was validated by experiments on the DOTA and HRSC2016 datasets, demonstrating that SA3Det achieves mAPs of 76.31% and 89.4%, respectively.

show abstract

YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes

Cited by 4 publications

References 17 publications

Data-set class-balancing and the Convolutional Vision Transformer

Data-set class-balancing and the Convolutional Vision Transformer

Image Inpainting using YOLOv8 and LaMa Model

SA3Det: Detecting Rotated Objects via Pixel-Level Attention and Adaptive Labels Assignment

Contact Info

Product

Resources

About