Paper Citation Record · LEDGER

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices

As of 21 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.07205.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.07205 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:05:16.947468Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:56:03.763652Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T17:56:04.255913Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy20
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad16c0b5-7465-4c93-b8e2-c6c1fea6f794 · outbound

This paper cites Image-Based Crack Detection Methods: A Review,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Image-Based Crack Detection Methods: A Review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.381384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 956516b5-42e4-4aa0-939b-7056f9ce22fc · outbound

This paper cites Autonomous UA Vs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo-Tagging,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Autonomous UA Vs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo-Tagging,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.364446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7b7893a5-deb8-48cc-a983-42f7f7e1a8a3 · outbound

This paper cites Automatic Road Pavement Assessment with Image Processing: Review and Comparison,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Automatic Road Pavement Assessment with Image Processing: Review and Comparison,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.347824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c2ea815c-8fce-46c5-872a-c94ff8746098 · outbound

This paper cites Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour Model,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour Model,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.333629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e72cb41e-2338-4b47-9cee-cc582463774f · outbound

This paper cites Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.321049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9b9ba984-3e85-4e62-9a0f-c023e7014bfe · outbound

This paper cites CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.305097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 68586324-7c01-443f-8707-c29aeac88323 · outbound

This paper cites CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.291193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7f8728d4-7792-4f20-bcab-a9c88c08bf71 · outbound

This paper cites Sam-based instance segmentation models for the automation of structural damage detection,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Sam-based instance segmentation models for the automation of structural damage detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.272735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 39baa5a2-71e5-44df-8c0c-3e9d6db26dcf · outbound

This paper cites Fine-tuning vision foundation model for crack segmentation in civil in- frastructures,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Fine-tuning vision foundation model for crack segmentation in civil in- frastructures,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.256181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8fc24aa0-e22e-4516-b7b9-ccf26394ea60 · outbound

This paper cites A Comprehensive Survey on Segment Anything Model for Vision and Beyond.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices A Comprehensive Survey on Segment Anything Model for Vision and Beyond

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T19:05:16.849036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9564a54-d12a-49c1-9228-a8998ae6e1cb · outbound

This paper cites Climbing robots for maintenance and inspec- tions of vertical structures—A survey of design aspects and technologies,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Climbing robots for maintenance and inspec- tions of vertical structures—A survey of design aspects and technologies,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.242569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ecc1f372-0ccd-4e36-8347-e8ad9b59393b · outbound

This paper cites A UA V-based crack inspection system for concrete bridge monitoring,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices A UA V-based crack inspection system for concrete bridge monitoring,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.225012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 68d0ad96-1477-4a11-8143-c5faab886b30 · outbound

This paper cites SDDNet: Real-Time Crack Segmentation,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices SDDNet: Real-Time Crack Segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.206944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ccca1141-2ee3-4af4-a9f4-412310355b32 · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Parameter-Efficient Transfer Learning with Diff Pruning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T19:05:16.882529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9093173-ad32-40f2-8922-3d5e8e959f91 · outbound

This paper cites Parameter-efficient transfer learning for NLP,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Parameter-efficient transfer learning for NLP,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.182890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 68ce30c4-4469-4d23-bec3-82cc284e3e5d · outbound

This paper cites SAM-Adapter: Adapting Segment Anything in Un- derperformed Scenes,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices SAM-Adapter: Adapting Segment Anything in Un- derperformed Scenes,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.167921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 19da87f7-ecec-4cd4-bd72-216ab9e4557b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T19:05:16.898061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7be5402-2de8-40c9-91ab-59412d854243 · outbound

This paper cites Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T19:05:16.902361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2d2d5fc-ebba-40a4-805e-73502d721e82 · outbound

This paper cites Segment Anything,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Segment Anything,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.153036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6b861ab9-f62f-41a7-ac41-2e9ca44d0d0b · outbound

This paper cites Fast Segment Anything.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Fast Segment Anything

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T19:05:16.910654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb1fb963-e5a3-4b8f-b348-a36d9483dc31 · outbound

This paper cites EdgeSAM: Prompt-In-the-Loop Distillation for SAM.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices EdgeSAM: Prompt-In-the-Loop Distillation for SAM

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T19:05:16.915776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 378ec840-f52d-4bcd-bb9c-14b89c0741ef · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T19:05:16.920347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56b98914-5a9d-41af-9544-a75e4c44570b · outbound

This paper cites YOLOv8 by Ultralytics,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices YOLOv8 by Ultralytics,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.137983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3e1036b5-d35e-4d2c-80f2-c6f90c9b5096 · outbound

This paper cites ConvLoRA and AdaBN Based Domain Adaptation via Self-Training,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices ConvLoRA and AdaBN Based Domain Adaptation via Self-Training,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.124738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d1646cb-5501-4aeb-a774-420d80e607ec · outbound

This paper cites khanhha/crack segmentation,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices khanhha/crack segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.109709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ba3c7fe0-f8f2-4937-ba8f-1b2de1a4d4bc · outbound

This paper cites Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detec- tion,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detec- tion,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.093102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3d7463d3-c5aa-49ae-8305-dd48de5f2ccb · outbound

This paper cites Robosense-Robotic delivery of sensors for seismic risk assessment,.

CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Robosense-Robotic delivery of sensors for seismic risk assessment,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:05:17.079013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 78dd3b14-746a-4886-92de-99dcaa8b78ec · inbound

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.259814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.