Paper Citation Record · LEDGER

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.21635.

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

pith.paper-citation-record.v1
2506.21635 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:54:22.077561Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact0
verified fuzzy22
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation daa6295f-7835-4f1c-a662-012756bb1bb4 · outbound

This paper cites The application of uav for the analysis of geological hazard in krk island, croatia, mediterranean sea,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing The application of uav for the analysis of geological hazard in krk island, croatia, mediterranean sea,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:28.682574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 94f6e812-a354-4f89-aaab-ca6acbedb377 · outbound

This paper cites Survey of autonomous drone hangars–opportunities and challenges for maritime platforms,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Survey of autonomous drone hangars–opportunities and challenges for maritime platforms,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:28.497520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e21b3031-a48d-481d-8fb3-1ddf60438b43 · outbound

This paper cites Estimation techniques in robust vision-based landing of aerial vehicles,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Estimation techniques in robust vision-based landing of aerial vehicles,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:28.246884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b571d567-f647-416d-aacb-1ed4588390e5 · outbound

This paper cites Consumer-grade global positioning system (gps) accuracy and reliability,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Consumer-grade global positioning system (gps) accuracy and reliability,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:27.988230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9360f61c-6614-4902-9560-05db95ac5c3c · outbound

This paper cites Dino: Detr with improved denoising anchor boxes for end-to- end object detection,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Dino: Detr with improved denoising anchor boxes for end-to- end object detection,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:18.052455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 123dc84c-cd44-4043-85ac-3a860e1cf020 · outbound

This paper cites Autonomous landing for a multirotor uav using vision,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Autonomous landing for a multirotor uav using vision,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:27.620600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2ccd44d6-34fa-47bc-b184-f7a63c48de2a · outbound

This paper cites Implementation of vision-based real time helipad detection system,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Implementation of vision-based real time helipad detection system,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:27.291580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f62ab462-f7cc-4f61-abbb-52c87628e5d6 · outbound

This paper cites Embedded vision system for auto- mated drone landing site detection,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Embedded vision system for auto- mated drone landing site detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:27.022038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fba0131a-a9b7-4647-8dcf-875a12e6aa8b · outbound

This paper cites Autonomous landing of uav based on artificial neural network supervised by fuzzy logic,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Autonomous landing of uav based on artificial neural network supervised by fuzzy logic,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:26.643556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 89d82147-fa9e-47fb-a058-8ad5a072fce7 · outbound

This paper cites Vision- based uav guidance for autonomous landing with deep neural networks,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Vision- based uav guidance for autonomous landing with deep neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:26.442907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 272bb2eb-a416-4521-9a31-e3cae864d743 · outbound

This paper cites A real-time semantic segmentation method based on stdc-ct for recognizing uav emergency landing zones,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing A real-time semantic segmentation method based on stdc-ct for recognizing uav emergency landing zones,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:26.157863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7f0c7922-78ad-4b63-9c80-a4184d526fa0 · outbound

This paper cites Swin-yolox for autonomous and accurate drone visual landing,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Swin-yolox for autonomous and accurate drone visual landing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:25.935309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aeb6610f-ac8d-483f-bd14-b230d9af9d33 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:19.293399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de3d1fbb-0a0f-47e9-8c45-c469269b632c · outbound

This paper cites Ssd: Single shot multibox detector,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Ssd: Single shot multibox detector,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:25.696909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 39a4ab9f-9a05-4f01-843f-7333bae674c0 · outbound

This paper cites Detrs with collaborative hybrid as- signments training,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Detrs with collaborative hybrid as- signments training,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:25.569574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation daedc00b-1b5d-45da-a22b-e0f7fe238a50 · outbound

This paper cites ultralytics/yolov5: v6. 0-yolov5n’nano’models, roboflow integration, tensorflow export, opencv dnn support,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing ultralytics/yolov5: v6. 0-yolov5n’nano’models, roboflow integration, tensorflow export, opencv dnn support,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:25.193808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 584fb41e-cc6e-4e30-87d0-04fc4cba3642 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Fully convolutional networks for semantic segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:24.800169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dfe1f7ae-12d3-4e02-a16f-c79ed7651b9a · outbound

This paper cites Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:20.077360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfc4b627-e247-4972-9346-b2a2c06a47ec · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:20.223758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7901f1c4-0a48-4ecc-af67-8e0bceac7052 · outbound

This paper cites Mask r-cnn,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Mask r-cnn,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:24.461642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7fe6f99d-e921-43d4-97d7-924d1ae9e415 · outbound

This paper cites Fast r-cnn,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Fast r-cnn,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:24.146085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 14a3d2e4-2ac9-4ff9-b224-7b0b0f563bd7 · outbound

This paper cites Rtmdet: An empirical study of designing real-time object detectors,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Rtmdet: An empirical study of designing real-time object detectors,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:20.687130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ca3642c-065f-48cd-9e41-e5c7c34f6975 · outbound

This paper cites Focal loss for dense object detection,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Focal loss for dense object detection,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:20.835051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45a2b381-02d9-4c2f-a062-6510ce34531c · outbound

This paper cites Enhancing geometric factors in model learning and inference for object detection and instance segmentation,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Enhancing geometric factors in model learning and inference for object detection and instance segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:23.752043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd4ca01f-7b2c-4d5f-bae4-7a0c6c2a8d23 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:23.460765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 30a1ae0d-04d9-4fbb-8171-072ad22b7b96 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing U-net: Convolutional networks for biomedical image segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:23.127955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d742c4dd-4986-41a1-9bee-efe7e9a9b1de · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image segmen- tation,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:21.604724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36690c90-de0c-4479-83c4-41bbeb52c8ca · outbound

This paper cites Mask r-cnn,.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Mask r-cnn,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:22.768098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b46d0db2-d86b-4a13-aca4-ba627ae1c8c1 · outbound

This paper cites ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:21.901862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 124e2cd7-6be0-44da-9ac6-5f38b834c131 · outbound

This paper cites Her research in- terests include high spatial resolution remote sensing image classification, change detection, and neural networks.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing Her research in- terests include high spatial resolution remote sensing image classification, change detection, and neural networks

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:54:22.422680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.