Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation | Paper Citation Record

Observation f7729bdb-4615-4be6-968f-cc40d85ef720 · outbound

This paper cites The Computational Limits of Deep Learning.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation The Computational Limits of Deep Learning

Reference 1

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Observation dca162f5-fea7-49e9-879a-0bb7273d0db0 · outbound

This paper cites Self-attention and long-range relationship capture network for underwater object detection,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Self-attention and long-range relationship capture network for underwater object detection,

Reference 2

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Observation bfa176ac-680e-45b1-894b-8d36bd2e53b2 · outbound

This paper cites An improved yolov5-based underwater object-detection framework,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation An improved yolov5-based underwater object-detection framework,

Reference 3

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Observation 197995b5-0a20-4977-b5e4-fd8f89d90107 · outbound

This paper cites Two-stage underwater object detec- tion network using swin transformer,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Two-stage underwater object detec- tion network using swin transformer,

Reference 4

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This paper cites Underwater object detection method based on improved faster rcnn,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Underwater object detection method based on improved faster rcnn,

Reference 5

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Observation e30df7b0-05da-468a-b819-f2a5315d4209 · outbound

This paper cites Yolo-dafs: A composite-enhanced un- derwater object detection algorithm,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Yolo-dafs: A composite-enhanced un- derwater object detection algorithm,

Reference 6

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Observation 321935c1-3f83-4b0b-a8a8-9eed8c4043d1 · outbound

This paper cites An improved yolov9s algorithm for underwater object detection,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation An improved yolov9s algorithm for underwater object detection,

Reference 7

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Observation da0116ba-1257-420d-a8b2-6a0a3811859f · outbound

This paper cites Bi2f-yolo: A novel framework for underwater object detection based on yolov7,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Bi2f-yolo: A novel framework for underwater object detection based on yolov7,

Reference 8

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Observation 7b87a184-0d4d-464d-bfd0-804e4ef6ed7a · outbound

This paper cites Yolov7-chs: An emerging model for underwater object detection,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Yolov7-chs: An emerging model for underwater object detection,

Reference 9

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Observation f679068f-019f-4997-accd-82687706ac31 · outbound

This paper cites YOLOv12: Attention-Centric Real-Time Object Detectors.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation YOLOv12: Attention-Centric Real-Time Object Detectors

Reference 10

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Observation 356c03af-9e3b-4722-bc02-c060f706b7ed · outbound

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Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Unresolved cited work

Reference 11

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Observation 1ce19db5-0141-48f9-9358-d9ce72f318d7 · outbound

This paper cites Speed/accuracy trade-offs for modern convolutional object detectors.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Speed/accuracy trade-offs for modern convolutional object detectors

Reference 12

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Observation 63d32bb2-894b-46bc-ab66-097cdc9b458f · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 13

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Observation d0079c55-769f-47d6-a581-6f7ed98eab7c · outbound

This paper cites Fast r-cnn,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Fast r-cnn,

Reference 14

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This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 15

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Observation db03cfe3-1007-41f3-9d74-8664c81db54a · outbound

This paper cites You Only Look Once: Unified, Real-Time Object Detection.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation You Only Look Once: Unified, Real-Time Object Detection

Reference 16

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Observation 5bce7fe8-6120-4c8c-81ab-a8d8bb7b5a38 · outbound

This paper cites Ssd: Single shot multibox detec- tor,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Ssd: Single shot multibox detec- tor,

Reference 17

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Observation e1f9148a-938e-45ed-a490-e22a2cb754fc · outbound

This paper cites FSSD: Feature Fusion Single Shot Multibox Detector.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation FSSD: Feature Fusion Single Shot Multibox Detector

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:59.450292Z

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Observation 6d709157-a27d-4ce0-a44c-605b893dc47b · outbound

This paper cites Object detection system based on ssd algorithm,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Object detection system based on ssd algorithm,

Reference 19

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Observation 7f92cf8e-beeb-4b01-bb84-3f38d7ca03a2 · outbound

This paper cites Focal Loss for Dense Object Detection.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Focal Loss for Dense Object Detection

Reference 20

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Observation 3c37867a-38a2-4e8b-a966-da188b9d658a · outbound

This paper cites End-to-End Object Detection with Transformers.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation End-to-End Object Detection with Transformers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T21:43:59.708389Z

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Observation f3fc9f20-393f-49c0-bbb7-6dcd6fbed55a · outbound

This paper cites Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future

Reference 22

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Observation f68b9f6e-689c-4daa-8f9f-67dd101fb183 · outbound

This paper cites Variational image dehazing with a novel underwater dark channel prior,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Variational image dehazing with a novel underwater dark channel prior,

Reference 23

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Observation b87769fb-b089-4c6e-b001-93397a51d165 · outbound

This paper cites Underwater image enhancement of ROV usingmodifiedWaterNet,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Underwater image enhancement of ROV usingmodifiedWaterNet,

Reference 24

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Observation c9e5ce8a-7178-45a4-b0db-9eefd5edc1c1 · outbound

This paper cites Underwater Image Enhancement using Generative Adversarial Networks: A Survey.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Underwater Image Enhancement using Generative Adversarial Networks: A Survey

Reference 25

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Observation 376c9b64-6b0e-4b06-b151-623db2a2890f · outbound

This paper cites An unsupervised underwater image en- hancement method based on generative adversarial networks with edge extraction,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation An unsupervised underwater image en- hancement method based on generative adversarial networks with edge extraction,

Reference 26

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Observation 9824308b-7743-4e57-ab87-c89fda839c9b · outbound

This paper cites New underwater image enhancement algorithm based on improved u-net,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation New underwater image enhancement algorithm based on improved u-net,

Reference 27

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Observation ee9cf1a3-1515-48f3-b0bc-631daf5e76d0 · outbound

This paper cites Yolov5-based enhanced underwater seaweed detection using open-source datasets,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Yolov5-based enhanced underwater seaweed detection using open-source datasets,

Reference 28

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Observation 8fd26cd4-84bb-4170-b9eb-24c5bcfd4583 · outbound

This paper cites Feb-yolov8: A multi-scale lightweight detec- tion model for underwater object detection,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Feb-yolov8: A multi-scale lightweight detec- tion model for underwater object detection,

Reference 29

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Observation 266115df-e5fd-4245-b1b9-165c4e626dc0 · outbound

This paper cites Cstc-yolov8:Underwaterobject detection model based on improved yolov8 for side scan sonar images,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Cstc-yolov8:Underwaterobject detection model based on improved yolov8 for side scan sonar images,

Reference 30

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This paper cites You only look once: Unified,real-timeobjectdetection,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation You only look once: Unified,real-timeobjectdetection,

Reference 31

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Observation d619d2c6-7747-489b-8555-4fa7f7068b8e · outbound

This paper cites an unresolved cited work.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Unresolved cited work

Reference 32

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Observation ceb66b7b-d5f4-4597-aaf7-15327a59da17 · outbound

This paper cites Refining features for underwater object detection at the frequency level,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Refining features for underwater object detection at the frequency level,

Reference 33

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:44:04.919024Z

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Observation 1a52730e-57e1-498f-9a9c-ed7e80cebebb · outbound

This paper cites A new dataset, poisson gan and aquanet for underwater object grabbing,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation A new dataset, poisson gan and aquanet for underwater object grabbing,

Reference 34

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Observation fbd38d2e-3a2c-4576-beb1-5f203cc28d91 · outbound

This paper cites Detectionofmarineanimalsinanewunderwaterdatasetwithvaryingvis- ibility,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Detectionofmarineanimalsinanewunderwaterdatasetwithvaryingvis- ibility,

Reference 35

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Observation c0cfe8cb-e072-4be8-be6a-7f0659f1b569 · outbound

This paper cites A dataset and benchmark of underwater object detection for robot picking,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation A dataset and benchmark of underwater object detection for robot picking,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T21:44:01.087563Z

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Observation 1a113713-7dff-43dc-8287-2256f1ac460b · outbound

This paper cites Scr-net: A novel lightweight aquatic biological detection network,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Scr-net: A novel lightweight aquatic biological detection network,

Reference 37

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Observation da058961-6b9c-4cff-92f3-3a7ea194533e · outbound

This paper cites Lfn-yolo: Precision underwater small object detection via a lightweight reparameterized approach,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Lfn-yolo: Precision underwater small object detection via a lightweight reparameterized approach,

Reference 38

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:44:04.599448Z

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Observation b96f1047-3f42-4078-8e98-5328783c663b · outbound

This paper cites Underwa- ter object classification and detection: First results and open challenges,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Underwa- ter object classification and detection: First results and open challenges,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:06.174898Z

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Observation b49aa0bb-ca5c-44e2-9df7-75efe4884ffc · outbound

This paper cites Lightweight underwa- ter object detection based on yolo v4 and multi-scale attentional feature fusion,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Lightweight underwa- ter object detection based on yolo v4 and multi-scale attentional feature fusion,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:06.012745Z

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Observation 6dd61a57-c230-443f-a60e-092f4a7002fe · outbound

This paper cites Yolov8-mu: An improved yolov8 underwater detector based on a large kernel block and a multi-branch reparameterization module,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Yolov8-mu: An improved yolov8 underwater detector based on a large kernel block and a multi-branch reparameterization module,

Reference 41

Resolution: verified exact; doi, observed 2026-08-06T21:44:02.729409Z

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Observation 3d848e03-eebd-45fb-960c-3ae0f615af32 · outbound

This paper cites SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:44:02.526293Z

Source-reported events for the cited work

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Observation 3fc2769c-ea9a-486d-b88c-1b8c41c288d0 · outbound

This paper cites EPBC-YOLOv8: An efficient and accurate improved YOLOv8 underwater detector based on an attention mechanism.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation EPBC-YOLOv8: An efficient and accurate improved YOLOv8 underwater detector based on an attention mechanism

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:44:04.426029Z

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Observation dafcae7f-cbd6-472e-8678-879123e7000e · outbound

This paper cites Vanilla-Yolo: a lightweight underwater object detector via reparameterization and multi-scale feature fusion,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Vanilla-Yolo: a lightweight underwater object detector via reparameterization and multi-scale feature fusion,

Reference 44

Resolution: malformed identifier; no resolver link, observed 2026-08-06T21:44:01.819766Z

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Observation e993aba5-d48a-4b66-90fa-ad44b32767de · outbound

This paper cites Multi-scale feature enhancement method for underwater object detection,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Multi-scale feature enhancement method for underwater object detection,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:05.837870Z

Source-reported events for the cited work

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Observation 1ef75a95-e694-4a7b-9ef9-f8a7c9d7ac0b · outbound

This paper cites U-decn: End-to-end underwater object detec- tionconvnetwithimproveddenoisingtraining,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation U-decn: End-to-end underwater object detec- tionconvnetwithimproveddenoisingtraining,

Reference 46

Resolution: verified exact; raw_fallback, observed 2026-08-06T21:44:04.212760Z

Source-reported events for the cited work

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Observation 56f3ba70-c40f-43cf-89c5-f3d18abc3543 · outbound

This paper cites Mas-yolov11: An improved underwater object detection algorithm based on yolov11,.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation Mas-yolov11: An improved underwater object detection algorithm based on yolov11,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:44:05.648538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c6f738f8-43e8-4d2a-9c84-9f647c653df5 · outbound

This paper cites [Online].

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation [Online]

Reference 8220

Resolution: verified exact; doi, observed 2026-08-06T21:44:02.352519Z

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