📄
Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model paper by Independent / Community
⭐ 66.8
📄
Paper

Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model

by Independent / Community 0005daf2eeeab8db1be36abe11207ada21a1b806
Free2AITools Nexus Index
66.8
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 79
P: Popularity 54
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Solid waste management is a significant challenge in the development of smart cities. Existing approaches for solid waste monitoring are often time‐consuming and resource intensive. Therefore, this study proposes a novel approach to solid waste monitoring that utilizes drone technology. The proposed method enables the efficient identification and classification of waste objects in the garbage discovered by the drone. This system can inspect every part of a smart city from a remote location, a...

Source →
Semantic Scholar 17 Citations
Paper Information Summary
Entity Passport
Registry ID 0005daf2eeeab8db1be36abe11207ada21a1b806
License ArXiv
Provider semantic_scholar
📜

Cite this paper

Academic & Research Attribution

BibTeX 
@misc{0005daf2eeeab8db1be36abe11207ada21a1b806,
  author = {Unknown},
  title = {Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0005daf2eeeab8db1be36abe11207ada21a1b806}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model [Paper]. Free2AITools. https://api.semanticscholar.org/0005daf2eeeab8db1be36abe11207ada21a1b806

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 79
Popularity (P) 54
Recency (R) 100
Quality (Q) 65

💬 Index Insight

FNI V2.0 for Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model: Authority (A:79), Popularity (P:54), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

📝 Executive Summary

"Solid waste management is a significant challenge in the development of smart cities. Existing approaches for solid waste monitoring are often time‐consuming and resource intensive. Therefore, this study proposes a novel approach to solid waste monitoring that utilizes drone technology. The proposed method enables the efficient identification and classification of waste objects in the garbage discovered by the drone. This system can inspect every part of a smart city from a remote location, a..."

❝ Cite Node 

@article{Unknown2026Efficient,
  title={Efficient solid waste inspection through drone‐based aerial imagery and TinyML vision model},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈17CitationsSemantic Scholar
🏛️79AuthorityFNI pillar
⏱️100RecencyFNI pillar
✅65QualityFNI pillar
🗂️vision multimediaField
📦Data Source: semantic_scholar
🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

⚙️ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

📊 Engagement & Metrics

downloads
0
stars
null
forks
null
citations
17

Data indexed from public sources. Updated daily.